Signal decomposition, analysis and reconstruction

ABSTRACT

The present invention provides a system and method for representing quasi-periodic (“qp”) waveforms comprising, representing a plurality of limited decompositions of the qp waveform, wherein each decomposition includes a first and second amplitude value and at least one time value. In some embodiments, each of the decompositions is phase adjusted such that the arithmetic sum of the plurality of limited decompositions reconstructs the qp waveform. These decompositions are stored into a data structure having a plurality of attributes. Optionally, these attributes are used to reconstruct the qp waveform, or patterns or features of the qp wave can be determined by using various pattern-recognition techniques. Some embodiments provide a system that uses software, embedded hardware or firmware to carry out the above-described method. Some embodiments use a computer-readable medium to store the data structure and/or instructions to execute the method.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/220,679, filed Aug. 29, 2011 (which issued as U.S. Pat. No. 8,386,244on Feb. 26, 2013), titled “SIGNAL DECOMPOSITION, ANALYSIS ANDRECONSTRUCTION,” which is a divisional of U.S. patent application Ser.No. 12/760,554, filed Apr. 15, 2010 (which issued as U.S. Pat. No.8,010,347 on Aug. 30, 2011), titled “SIGNAL DECOMPOSITION, ANALYSIS ANDRECONSTRUCTION APPARATUS AND METHOD,” which is a divisional of U.S.patent application Ser. No. 11/360,135, filed Feb. 23, 2006 (whichissued as U.S. Pat. No. 7,702,502 on Apr. 20, 2010), titled “APPARATUSFOR SIGNAL DECOMPOSITION, ANALYSIS AND RECONSTRUCTION,” which claimedbenefit of U.S. Provisional Patent Application 60/656,630, filed Feb.23, 2005, titled “SYSTEM AND METHOD FOR SIGNAL DECOMPOSITION, ANALYSISAND RECONSTRUCTION,” each of which is incorporated herein by referencein its entirety. This application is related to U.S. patent applicationSer. No. 11/360,223, filed Feb. 23, 2006 (which issued as U.S. Pat. No.7,706,992 on Apr. 27, 2010), titled “SYSTEM AND METHOD FOR SIGNALDECOMPOSITION, ANALYSIS AND RECONSTRUCTION,” which is incorporatedherein by reference in its entirety.

FIELD OF THE INVENTION

This invention relates to the field of computer-implemented systems andmethods, and more specifically a software-, embedded-circuits- orfirmware-implemented system and method to decompose signals havingquasi-periodic wave properties, store such signals in a data structure,analyze such signals, and reconstruct such signals from the datastructure, and to optionally transmit such data structure over acommunications channel.

BACKGROUND OF THE INVENTION

The digital representation of waveforms is a technology that is centralto various sectors of industry where the detection of periodic andnon-periodic waveforms can be critical to determining whether an erraticheartbeat, electrical short circuit, or some other problem exists. Adigital representation must clearly and accurately represent the analogsource of a waveform, but at the same time be able to accomplish suchthings as, compressing the incoming data into some manageable size, andmaintain the integrity of the incoming data (i.e., making sure that thedigital representation has enough fidelity to the original signal to beuseful). Of additional import is the ability to have a digitalrepresentation that can consistently allow one to identify the presenceand location of certain wave features, and/or that lends itself tocertain types of automated analyses.

High-fidelity digital representations are problematic for a number ofreasons. First, they require relatively large amounts of space withinwhich to store the digitized data. Put another way, the higher thefidelity of the digitized data, the larger the amount of storage needed.Another problem with high-fidelity digital representations is that theycan result in large amounts of digital data that has little or no importin terms of conveying meaning. For example, a periodic wave signal thatmerely repeats the same waveform does not convey much meaning to theperson analyzing the waveform, and may in fact just take up storagespace with unremarkable data. An additional problem is the repeatedsampling, over sampling of such high-fidelity data even though it isotherwise unremarkable. Such over sampling results in wasted processingbandwidth (i.e., processor cycles, and/or power) as well as databandwidth (data storage space and/or transmission bandwidth).

One solution to the above-cited problems associated with a high-fidelitydigital representation is to devise a method for filtering andconcentrating this high-fidelity waveform data in such a manner that theintegrity and significant features of the data are maintained, but whileutilizing lower data bandwidth and lower processing bandwidth. Thus, oneneeds a method and structure that can efficiently and accurately capturethe underlying waveform, with little or no degradation of the value andmeaning of that waveform data.

SUMMARY OF THE INVENTION

Some embodiments of the present invention provide a system and methodfor representing a quasi-periodic (qp) waveform including: representinga plurality of limited decompositions of the qp waveform, wherein eachdecomposition includes a first and second amplitude value and at leastone time value associated with the first amplitude value. In someembodiments, at least one time value is associated with a secondamplitude value, and each of the decompositions is phase adjusted suchthat the arithmetic sum of the plurality of limited decompositionsreconstructs the qp waveform.

In some embodiments, the present invention provides a method foranalyzing qp waveforms including an input/analysis process that takes anoriginal qp waveform signal and produces from it a stream of descriptiveobjects. Within the input/analysis process, a signal-decompositionprocess decomposes the original quasi-periodic signal into a set ofcorresponding component signals (herein these are also called “componentwaves,” “decomposition components,” or simply “components”). Thecomponent signals are then processed into a fractional-phaserepresentation to identify the object boundaries and measure basicattributes. The corresponding object-related information is then passedto an object-construction-and-linking process that constructs a streamof objects, filling out whatever additional object-related and/ordata-structure-related information is needed for a particularapplication. An interpretive process is then applied that takes thestream of objects and produces an interpretation of the original qpwaveform signal. Within the interpretive process, a state constructorthat takes the stream of objects and constructs states from them isapplied. Further, an organized mapping process is utilized, where theinformation is mapped in state space that includes both ad hoc andstructured mapping utilizing Hidden Markov Models and Neural Networks.Additionally, an organized mapping process is applied, where theinformation is mapped in a vector space along one or more objectattributes and mapping utilizing vector-space methods, including HiddenMarkov Models or Neural Networks. Furthermore, a pattern-recognitiondiscrimination process to transform the mapped information into ahuman-interpretable form is applied. Then a storage process takes theobject stream and samples of this object stream and stores some or allof the samples in a memory. Optionally, a storage process takes theobject stream and transmits some or all of the objects over acommunications link. In some embodiments, a re-synthesis/output processtakes the object stream from a communications link and/or storage andreconstructs an estimate of the original signal(s).

In some embodiments, the method further includes placing the first andsecond amplitude values, the first time value, and the second time valuein a data structure.

Some embodiments further include a computer-readable medium havingstored thereon a data structure optionally three or more fieldsincluding: a first field containing an object ID, a second fieldcontaining a T4 value, a third field containing a phase label, a fourthfield containing an amplitude measure, a fifth field containing thetotal time elapsed, a sixth field containing a relative amplitude value,a seventh field containing a relative T4 value, and an eighth fieldcontaining an atomic period value, a ninth field containing a memoryaddress of a left neighbor, a tenth field containing a memory address ofa right neighbor, an eleventh field containing a memory address of aleft object, a twelfth field containing a memory address of a rightobject, a thirteenth field containing a left harmonic, a fourteenthfield containing a right harmonic, a fifteenth field containing a memoryaddress for an upper neighbor, a sixteenth field containing a memoryaddress for a lower neighbor, a seventeenth field containing datarelating to cyclic regularity, an eighteenth field containing datarelated to a left local period time, a nineteenth field containing datarelated to a right local period time, a twentieth field containing datarelated to a left harmonic, a twenty-first field containing data relatedto a right harmonic, and a twenty-second field related to amplitudemodulation.

Other embodiments include a computer-readable medium having executableinstructions stored thereon for causing a suitable programmed centralprocessor to perform a method including: taking input in the form of abinary representation of a base waveform, passing this binaryrepresentation through a series of bandpass filters, taking thecomponent-signal output of the bandpass filters and extracting data fromthese components waves, and storing the data into a plurality of datastructures. The method further including organizing the data structure.The method further including outputting the data in the plurality ofdata structures in the form of a graphical representation. The methodfurther including processing the data in the data structure using aHidden Markov model. The method further including processing the data inthe data structure using a Neural Network.

In other embodiments, the invention provides a system for analyzing qpwaves including: a database containing data related to qp waves, a CPUoperatively coupled to the database. A computer-readable mediumoperatively coupled to the CPU containing instructions in someCPU-readable format or language, which allows the CPU to: take input inthe form of a binary representation of a base waveform, passing thisbinary representation through a series of bandpass filters, taking acomponent wave output of the bandpass filters, extracting data fromthese components waves, and storing the data into a plurality of datastructures. Additionally, an input device is operatively coupled to theCPU, an output device is operatively coupled to the CPU, the CPUoperatively coupled to an internet, and a terminal for receiving datafrom the data structures via the internet.

In some embodiments, the invention provides a system for analyzing qpwaves. This system includes a set of electrodes each with one endoperatively connected to the skin surface or disposed below the skinsurface of the body, an optional set of electrodes at least one end ofwhich is operatively connected to the heart itself, a second end of aset of electrodes operatively coupled to an analog-to-digital converter,the analog-to-digital converter operatively coupled to a microprocessorunit, a computer-readable medium containing program instructionsoperatively coupled to the microprocessors unit, a memory storageoperatively coupled to the microprocessor unit, a communications moduleoperatively coupled to the microprocessor unit, and a power supplyoperatively coupled to the analog-to-digital converter, microprocessorunit and communications module. The system further including: acommunications module, a microprocessor unit, a computer-readable mediumcontaining a set of application instructions, an associated storagemodule containing data values representing the original signal, aHuman-Computer Interface by which an operator may interact with themicroprocessor unit, an input device, and an output device.

In some embodiments, the invention is implemented using a multiplierlessarchitecture in conjunction with embedded circuits. In still otherembodiments, the invention is implemented using firmware, and in stillother embodiments the invention is implemented using software.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a parallel filter bank 100.

FIG. 2 is a block diagram 200 in exploded form illustrating the cascadeconstruction of transfer function H_(p) 106.

FIG. 3 is a block diagram 300, in exploded form, which shows derivativekernel block Hd_(p) 201 as constructed from a cascade of N_(d) basicderivative kernel blocks Kd_(p) 301.

FIG. 4 is a block diagram 400, in exploded form, which shows integratorkernel block Hi_(p) 202 as constructed from a cascade of N_(i) basicintegrator kernel blocks Ki_(p) 401.

FIG. 5 is a block diagram of a cascade filter bank 500.

FIG. 6 is a block diagram 600 showing a section of an analytic filterbank.

FIG. 7A is a waveform 700 illustrating a cycle from one component signalthat is broken up into four quarter-phases.

FIG. 7B is a table 751 illustrating the various unique features such asthe positive-going and negative-going zero crossings, and the positiveand negative peaks of the component signal that are analyzed by theinvention.

FIG. 7C shows how four quarter-phases (i.e., reference numbers 701, 702,703, 704) of waveform 700 may each be characterized simply using twoparameters.

FIG. 7D shows a method 752 for breaking up into quarter-phases thecycles of two component signals, the time ranges over which the phaselabels are coincidentally stable (quarter-phases are active), and thestates defined by vectors of those labels, indicated here with labelsAa, Ab, Ac, Bc, Bd, Cd, Ca, Cb, Db, Dc, Dd, etc.

FIG. 7E is a matrix 753 containing the sixteen possible quarter-phaseregions (states) of a method 700, for two component waves.

FIG. 8A is a diagram of a data structure 800.

FIG. 8B is a diagram of a data structure 851.

FIG. 8C is a diagram of a data structure 852.

FIG. 9A is a state matrix 900 depicting state transitions arising fromtransitions between pairs of quarter-phases.

FIG. 9C is a simplified state-transition matrix 902 of the availablestates for a system of two components.

FIG. 9B is a state-transition matrix 951 depicting state transitionsarising from transitions between pairs of quarter-phases.

FIG. 9D represents a simplified state-transition matrix 952, in tableform.

FIG. 10A is a graph 1000 depicting a plot of the input ECG signal andthe reconstruction sums of the real parts and the imaginary parts of thecomponents resulting from a parallel-form analytic filter-bankdecomposition of the input ECG signal.

FIG. 10B is a graph 1001 of the analytic components, one for each bandused in the example, with quarter-phase transition points marked.

FIG. 10C is graph 1002 of the real-part component sum reconstruction ofthe input ECG signal, with all quarter-phase transition points marked.

FIG. 10D is a graph 1003 of a specific segment taken from FIG. 10C inthe range of 260 msec to 420 msec, with quarter-phase transition pointsmarked and corresponding state labels denoted.

FIG. 11 shows a graphical illustration 1100 of the deconstruction of anormal sinus rhythm (NSR) 1101 into its component waveforms (i.e., Nos.1102, 1103, 1104, 1105, 1106, 1107).

FIG. 12 is a graphical illustration 1200 of the decomposition of anotherNSR electrocardiogram (ECG), along with a set of resulting componentsignals, in this case, those from component filters with centerfrequencies of 20, 10, 5, and 2.5 Hz respectively, from the topmostcomponent trace (i.e., Nos. 1201, 1202, 1203, 1204, and 1205).

FIG. 13 is an illustration 1300 of an example base quasi-periodic waveand corresponding components, and certain resulting quarter-phases andstates of interest that reveal subtle morphological changes from normalECG morphology due to an abnormal condition, in this case acutemyocardial infarction (MI) in a human patient.

FIG. 14 is an illustration 1400 of an example base quasi-periodic waveand corresponding components, and certain resulting quarter-phases andstates of interest, for a normal ECG morphology in a human patient.

FIG. 15A illustrates a segment 1500 of an original signal ECG anddeconstructs this segment by state.

FIG. 15B illustrate a segment 1500 at progressively higher stateresolution of 64, by adding states available due to quarter-phaseobjects from the 5-Hz component.

FIG. 15C illustrates a segment 1500 at progressively higher resolutionof 256, by adding states available due to quarter-phase objects from the10-Hz component.

FIG. 16 illustrates an ad-hoc pattern detection method 1600 fordetection of the fundamental cardiac cycle and the onset and offset ofthe QRS complex.

FIG. 17 depicts a method 1700 of displaying the decomposition of an ECGlead V2 into six (6) frequency bands.

FIG. 18 is an illustration of method 1800 of displaying thereconstruction of an ECG base wave from the state objects data.

FIG. 19 depicts a matrix representation of output for the originalsignal ECG and deconstructs this segment by state. Each box representsone member of the set of 64 possible states corresponding to thequarter-phase objects from the three lowest frequency components (i.e.,Nos. 1701, 1702, 1703). Many of the boxes each contain sets of linesegments overlaid, arising from multiple depolarizations of the heart.

FIG. 20 is a block diagram of a system 2000 wherein data related to a qpwaveform is stored in a database 2001.

FIG. 21 is a block diagram of a system 2100 wherein the original wavesignal is an ECG from a patient 2101 and including an ECG signalcollection and processing device 2103.

FIG. 22 is a block diagram of a system 2200 that depicts a host 2201.

FIG. 23 is a block diagram of a system 2300 of an Input/Analysis process2301, an Interpretive Process 2305, a Storage/Transmission block 2309,and a Resynthesis/Output process 2310.

DETAILED DESCRIPTION OF THE INVENTION

Although the following detailed description contains many specifics forthe purpose of illustration, a person of ordinary skill in the art willappreciate that many variations and alterations to the following detailsare within the scope of the invention. Accordingly, the followingpreferred embodiments of the invention are set forth without any loss ofgenerality to, and without imposing limitations upon the claimedinvention. Further, in the following detailed description of thepreferred embodiments, reference is made to the accompanying drawingsthat form a part hereof, and in which are shown by way of illustrationspecific embodiments in which the invention may be practiced. It isunderstood that other embodiments may be utilized and structural changesmay be made without departing from the scope of the present invention.

The leading digit(s) of reference numbers appearing in the Figuresgenerally corresponds to the Figure number in which that component isfirst introduced, such that the same reference number is used throughoutto refer to an identical component that appears in multiple Figures.Signals and connections may be referred to by the same reference numberor label, and the actual meaning will be clear from its use in thecontext of the description.

In some embodiments, the present invention provides a high-fidelitydigital representation by using a method for filtering and concentratinghigh-fidelity waveform data in such a manner that the integrity andsignificant features of the data are maintained, while utilizing lessstorage space and fewer processor cycles than conventional techniques.In some embodiments, the present method takes a high-fidelity wavesignal (i.e., a qp or the base wave signal), passes this signal througha filter bank that decomposes or separates out this signal into seriesof locally periodic component signals, and then stores these componentsignals and their associated characteristics into a data structure. Thisdata structure can then be used to reconstruct the base wave signal, tosearch for certain features or patterns within the wave signal, or avariety of other purposes. One advantage of the present method is thatinsignificant or undesirable data can be filtered out and prevented fromtaking up storage space. In some embodiments, the filters can beadjusted to allow certain wave features to be stored, while otherfeatures are disregarded. This ability to adjust filters has the addedadvantage that when a sample of wave data is taken, it is more likelythat the sampling will be of significant wave data, rather thaninsignificant wave data, thus resulting in fewer processor cycles neededfor wave sampling.

Another advantage of this method is that by storing the component wavesignals into a data structure, these signals can be easily utilized andmanipulated for a variety of purposes. For example, a component wavesignal can be analyzed for certain characteristics or patterns. Thesecharacteristics or patterns of the component wave signal can then becompared to the other components or the base waveform itself to give abetter idea of the significance of the characteristic or pattern.

Decomposition and Filter Banks-Generally

To begin to understand the use of filter banks in the decomposition of abase wave or original signal it is necessary to understand a base wavesignal (i.e., an original signal) as being quasi-periodic (qp).Quasi-periodic means that an original signal may be considered as thelinear combination of a series of locally periodic component signalshaving different periods and/or shapes. Locality here is taken to be onthe order of a few cycles of the component in question, though incertain cases can be as small as one cycle, such as, for example, if acertain transient condition to be detected or analyzed sporadicallyelicits such a component.

Starting with the assumption of a qp waveform, decomposition of theoriginal signal involves the local estimation of the qp components ofthe original signal. Many methods have been proposed, developed, and arewell established in the art. Examples of these are the Short-TimeFourier Transform (STFT), Wavelet Transform, and generally any methodthat may be cast as a bank of filters (Filter Bank). (See Wavelets andFilter Banks, by Gilbert Strang and Truong Nguyen, Wellesley College,1996.) The individual filters in the filter bank, herein calledcomponent filters or component bands, are designed to emphasize (i.e.,correlate to) a locally periodic component of interest in the originalsignal, and so will tend to have impulse responses that oscillate over alocal region of time. The frequency responses of the component filterswill tend to have relatively confined regions (passbands) where themagnitude is substantially higher than in other regions. The width ofthe passband (the bandwidth) is typically measured at the half-powerpoints (i.e., the difference of the two frequencies where the magnituderesponse is a factor of sqrt(2)/2 below the maximum value in between). Asingle component filter may have more than one passband, for example ifthe component is represented by a harmonic series. One may denote theoutputs of these component filters as the component outputs, or thecomponent signals.

The design of such filter banks is well understood and established inthe art. (See Multirate Digital Signal Processing: MultirateSystems—Filter Banks—Wavelets, by N. J. Fliege, John Wiley & Sons,2000.) Of specific interest here is that the component filters match, insome useful sense, the underlying local behavior of the original signal.Many approaches are readily available. For example, a high-resolutiontime-frequency analysis of the original signal could be performed tosimultaneously identify frequency bands and time-widths of interest,(i.e., by studying local peaks of signal energy on the time-frequencyplane). Alternatively, or in conjunction, an autoregressive movingaverage (“ARMA”) model could be generated of the original signal. TheARMA model, itself a filter, may be decomposed into component filtersusing pole and zero separation techniques, such as partial fractionexpansion, or any established transformation from cascade to parallelform. Multiple ARMA models could be formed by ARMA modeling each outputof a relatively coarse filter bank. Alternatively, or in conjunction, aKarhunen-Loeve Transform (KLT) or a Singular-Value Decomposition (SVD)could be performed on the convolution matrix or correlation matrixestimate formed from a finite time window of the original signal toeither directly produce a bank of filters, or to identify filters thatcorrelate to the principal components of the original signal (i.e., aPrincipal Components Analysis, or PCA). (See Matrix Computations, 3rdEdition, by G. H. Golub, and C. F. Van Loan, Johns Hopkins UniversityPress, 1996.) The identified components of interest could then be useddirectly or approximated with simpler wavelets. Generally, thedecomposition of the original signal may employ any linear or nonlinearmethod that produces a moving local estimate of the underlying locallyperiodic components of the original signal.

In some embodiments, prior knowledge of the original wave signal mayalso be used to define component bands. For example, in someembodiments, an electrocardiogram (“ECG”) signal is expected to have astrong component at the fundamental heart rate. Therefore, for example,in some embodiments, a component is designed having a passband fromroughly 0.5 Hz to 5 Hz, to represent a range of expected heart rates.

Generally, the time widths of the impulse responses of the filters inthe bank are inversely proportional to the bandwidths of their frequencyresponses, thus representing a tradeoff in time resolution and frequencyresolution. Of primary interest is that the time width be narrow enoughto be substantially representative of the duration over which acorresponding underlying locally periodic component will be locallystable, while still maintaining sufficient resolution in frequency. Suchtradeoffs are well understood and established in the art, and fall underthe class of Spectral Estimation. (See Spectral Analysis of Signals, byPetre Stoica and R. L. Moses, Prentice-Hall, 2005.)

One may define a reconstructed signal, or reconstruction, to be thesignal resulting from taking the sum of the component signals at eachpoint in time (i.e., the linear combination of the component signals ateach point in time is understood here as a “generalized sum”). In someembodiments, the component filters are designed such that thereconstructed signal is a satisfactory approximation of the originalsignal. The type and quality of the approximation, and therefore thequality of the signal that can be reconstructed, will depend stronglyupon the application, but may be measured, for example, usingwell-established error-distance measures, such as, for example,mean-square error (MSE). (See Some comments on the minimum mean squareerror as a criterion of estimation, by C. Radhakrishna Rao, Technicalreport/University of Pittsburgh-Department of Mathematics andStatistics-Institute for Statistics and Applications, 1980.) Theapproximation would need qualification, for example, in the case wherethe original signal was comprised of qp components plus wideband noise,and the reconstruction estimated the components while exhibitingsubstantially no noise.

Note that in the above sense, the frequency responses of the componentbands, when taken as a whole, need not cover the entire spectrum to thehighest frequency of interest. Frequency bands may be excluded in whichrelatively little signal energy exists and/or where a poorsignal-to-noise ratio (SNR) exists. Additionally, in some embodiments,the time and/or phase alignment of the component filter impulseresponses and/or the component signals is assumed to be understood aspart of this invention, for the purpose of producing or representing anadequate reconstruction, including the various well-understoodconditions for perfect and approximate reconstruction filter banks (seeWavelets and Subband Coding, M. Vetterli and J. Kovacevic,Prentice-Hall, 1995). The alignment may, for example, take into accountthe various delays inherent among the impulse responses in the filterbank. Such alignment may also be accounted for in the timingrepresentations embedded in the processing of the component signals,such as in the data structures, by recording any necessary time and/orphase offsets in conjunction with, or added to, any timing and/or phaseinformation for the components. If signal reconstruction is necessary,such timing information may thus be applied within the reconstructionprocess.

Moreover, in some embodiments, the impulse responses may be linear phase(symmetric/antisymmetric), minimum phase, or dispersive (havingasymmetric response but not linear phase), provided that they meet theabove signal-reconstruction criteria.

In all of the above filter-bank embodiments, an analytic form may beincluded, wherein both the above component signals and their Hilberttransforms are produced. This may be accomplished by any of the meanswell understood and established in the art. (See The Fourier Transformand Its Applications 3rd Edition, by R. Bracewell, McGraw-Hill, 1999.)For example, in some embodiments, the output of each component filter inthe bank is provided to the input of a Hilbert transformer, where thecomponent filter output is taken as the real part and the Hilberttransformer output is taken as the imaginary part of a resultinganalytic signal. In another embodiment, a second bank of componentfilters may be provided, each component filter having an impulseresponse that is the Hilbert transform of its corresponding componentfilter from the first bank. In either case, an analytic form of eachcomponent signal (e.g., an analytic component signal) is formed bytaking the component signal of each component filter as the real partand the Hilbert-transformed version of that component signal as theimaginary part. One may note that the STFT inherently produces ananalytic form for the components.

The Hilbert transform need not be ideal, only that the frequency-domainphase response of the effective component filter for the imaginary partbe offset by substantially 90 degrees from the phase response of thecorresponding component filter for the real part, and only overfrequency ranges where the frequency response magnitudes aresufficiently high to be of interest (e.g., above some arbitrarythreshold, say 20 dB below the peak magnitude). Of course, the frequencyresponse magnitudes of the real-imaginary filter pair should besubstantially equal over these frequency ranges, or at least equivalentup to some predetermined level of equivalence, based, for example, uponan error-distance measure or maximum allowable deviation in decibels.

These analytic component signals make available the instantaneousanalytic magnitude (i.e., the “magnitude” or “amplitude”) and theinstantaneous analytic phase of the component signal. These may becomputed, for each point in time of interest, from the magnitude andprincipal argument of the angle in the complex plane.

Decomposition and Filter Banks—the Present Invention

The following are example embodiments of the method of decomposition andthe filter banks utilized in this invention. In terms of notation,capital letters used for the transfer functions signify frequency-domaintransfer functions, but imply their time-domain counterparts as well.The converse also holds true (i.e., lower-case letters signifying theimpulse responses of the blocks also imply their correspondingfrequency-domain transfer-function counterparts). Additionally,cascading of blocks implies multiplication of their correspondingtransfer functions in the frequency domain and/or convolution of theircorresponding impulse responses in the time domain, as is wellestablished and understood in the art. (See Discrete-Time SignalProcessing, by A. V. Oppenheim, R. W. Schafer, and J. R. Buck,Prentice-Hall, 1999.)

Blocks are considered to be linear, time-invariant systems, and so in agiven implementation the blocks include a certain cascade of blocks andmay be rearranged in any order within the cascade. These filter banksmay be applied as a bank of filter sections arranged in a parallel formknown here as a Parallel-Form Kovtun-Ricci Wavelet Transform, and/orthese filter banks may be applied as a bank of filter sections arrangedin a cascade form known here in as a Cascade-Form Kovtun-Ricci WaveletTransform. For the purpose of this invention the below-described methodof decomposition, filter banks and algorithms utilized in these filterbanks, may be implemented in software, embedded circuits (hardware) orfirmware.

An Example of a Parallel Filter Bank

FIG. 1 is a block diagram of a parallel filter bank 100. FIGS. 1-4describe a method of signal decomposition using filter bank 100 having aparallel arrangement of N filter sections that use the Parallel-FormKovtun-Ricci Wavelet Transform. In some embodiments, parallel filterbank 100 is implemented in software, firmware, hardware, or combinationsthereof. An original signal x 101 is applied to the input of the bank,and a set of N component signals y₁ . . . y_(N) 104 are provided at theoutputs of the N filter sections (also called “component bands,” orsimply, “bands”) of filter bank 100. In some embodiments, the filtersections are each comprised of a cascade of a component filter and adelay element, with the component filters having transfer functionsdenoted by H₁ . . . H_(N) 102, and the delay elements having transferfunctions denoted by D₁ . . . D_(N) 103. The p^(th) filter section 105of filter bank 100, where p=1 . . . N, is thus comprised of componentfilter H_(p) 106 (one example of which is discussed further in thebelow-described FIG. 2) and delay element D_(p) 107. Original inputsignal x 101 is provided at the inputs of all filter sections of filterbank 100, each of which produces corresponding component signal y_(p)108, p=1 . . . N.

Component Filter

FIG. 2 is a block diagram 200 in exploded form illustrating oneembodiment of a cascade construction of H_(p) 106. In some embodiments,the invention provides a component filter transfer function for thep^(th) component filter H_(p) 106, p=1 . . . N. Transfer function H_(p)106 is constructed from a cascade of two major blocks: a derivativekernel block Hd_(p) 201 and an integrator kernel block Hi_(p) 202.

Scaled Derivative Kernel Block

FIG. 3 is a block diagram 300 in exploded form of one embodiment thatshows derivative kernel block Hd_(p) 201 as constructed from a cascadeof N_(d) basic derivative kernel blocks Kd_(p) 301. In the embodimentshown, a derivative kernel block Hd_(p) 201 is constructed from acascade of N_(d) basic derivative kernel blocks Kd_(p) 301 as shown. Thetransfer function Kd_(p) 301 generally implements an approximation ofthe time derivative. The parameter p, while generally serving as anindex to the p^(th) component band (and thus the p^(th) filter section),serves here also as an index to a predetermined scaling factor k_(p) onthe frequency scale of frequency response Kd_(p) 301, and/or on the timescale of the impulse response kd_(p) 301 of the basic derivative kernel.Frequency and/or time scaling, and its application and use, aregenerally understood and established in the art as per the theory ofWavelets, Wavelet Transforms, and Filter Banks for the purpose of tuningcertain characteristics of each component filter in the bank, such as,for example, the center frequency and/or bandwidth of the resultingcomponent filter. (See The Fractional Fourier Transform, withApplications in Optics and Signal Processing, by H. M. Ozaktas, Z.Zalevsky, and M. A. Kutay, Wiley, 2000; Wavelets and Subband Coding, M.Vetterli and J. Kovacevic, Prentice-Hall, 1995.)

An example embodiment of basic derivative kernel block Kd_(p) 301 isgiven by the following discrete-time impulse response description:kd _(p)(n)=(δ(n)−δ(n−k _(p)))/2Here the normalization by the factor 2 is a choice for this example toprovide a unity-gain passband, but may, in fact, be any arbitraryfactor, and where there is defined the discrete-time delta functionsometimes called the unit delta or unit impulse:

${\delta(n)} = \left\{ \begin{matrix}{1,} & {n = 0} \\{0,} & {n \neq 0}\end{matrix} \right.$In some embodiments, kd_(p) is comprised of, besides delay operations,the operations of signed addition and scaling by a factor of two,allowing for, in some embodiments, implementation using a multiplierlessarchitecture. Specifically, it is well understood in the art ofdigital-signal-processing architectures, that scaling a signal sample byany integer power P of 2 may be readily implemented in digital form withappropriate shifting by P bits the digital bit representation of thesignal sample. Thus, by extension through the cascade, scaled-derivativekernel block Hd_(p) may itself, in some embodiments, be implementedusing a multiplierless architecture. Further, it is worth noting thatthe time-domain impulse response hd_(p) of scaled-derivative kernelblock Hd_(p) 201 has generally a bell-shaped envelope, particularlyevident for N_(d) greater than or equal to about 4, withalternating-sign coefficients. All nonzero coefficients of hd_(p) have(k_(p)−1) zeros interpolated between them. Even values of N_(d) resultin hd_(p) symmetric about their time centers, while odd values of N_(d)result in hd_(p) antisymmetric about their time centers. The intrinsicdelay of the impulse response hd_(p), equivalent to the time delay tothe time center of hd_(p), is Dd_(p)=N_(d)k_(p)/2 samples, and thisdelay is integer-valued for N_(d) and/or k_(p) being even. The z-domaintransfer function of the scaled-derivative kernel block is:

${{Hd}_{p}(z)} = \left\lbrack \frac{1 - z^{- k_{p}}}{2} \right\rbrack^{N_{d}}$where, when evaluated on the unit circle (z=e^(jω)), the frequencyresponse is:

${{Hd}_{p}(\omega)} = \left\lbrack \frac{1 - {\mathbb{e}}^{{- j}\;\omega\; k_{p}}}{2} \right\rbrack^{N_{d}}$The frequency response Hd_(p)(ω) exhibits a series of substantiallybell-shaped passbands, particularly for N_(d) greater than or equal toabout 4, centered at ω_(pq)=π(1+2q)/k_(p) radians, orf_(pq)=Fs(1+2q)/(2k_(p)) Hz, where Fs is the sample rate in Hz, andwhere q=0, 1, 2, . . . , such that the q=0 case corresponds to thefundamental passband centered at ω_(p0)≡ω_(p) (f_(p0)≡f_(p)). Thepassbands have bandwidths that are inversely proportional to k_(p) andmonotonically inversely related to N_(d). In the fundamental passband,the frequency response has constant (“flat”) phase equal to N_(d)π/2,after accounting for the linear phase term due to the intrinsic delayDd_(p).

Scaled Integral Kernel Block

FIG. 4 is a block diagram 400 in exploded form that shows integratorkernel block Hi_(p) 202 as constructed from a cascade of N_(i) basicintegrator kernel blocks Ki_(p) 401. In this embodiment, integratorkernel block Hi_(p) 202 is constructed from a cascade of N_(i) basicintegrator kernel blocks Ki_(p) 401. The transfer function Ki_(p) 401generally implements an approximation of the local time integral (i.e.,the time integral over a local interval of time). The parameter p, whilegenerally serving as an index to the pth component band, serves herealso as an index to a predetermined scaling factor w_(p) on thefrequency scale of frequency response Ki_(p) 401 and/or on the timescale of the impulse response ki_(p) of the basic integrator kernel,where frequency and/or time scaling is generally understood as beforewith regard to the above derivative kernel. An example embodiment ofblock Ki_(p) 401 is given by the following discrete-time impulseresponse description:ki _(p)(n)=(u(n)−u(n−w))/w _(p)where the normalization by the factor w_(p) is a choice for this exampleto provide a unity-gain passband, but may in fact be any arbitraryfactor, and where there is defined the discrete-time unit step function:

${u(n)} = \left\{ \begin{matrix}{1,} & {n \geq 0} \\{0,} & {n < 0}\end{matrix} \right.$In some embodiments, ki_(p) is comprised of, besides delay operations,the operations of signed addition and scaling by a factor of w_(p). Instill other embodiments, choosing w_(p) as an integer power of 2 allowsfor implementation of ki_(p) using a multiplierless architecture, and byextension through the cascade, allows scaled-integral kernel blockhi_(p) to be implemented using a multiplierless architecture.

In some embodiments, the time-domain impulse response of basicintegrator-kernel block ki_(p) is rectangular in shape, with a width ofw_(p) samples and with height w_(p) ⁻¹, so that the gain at dc of thisblock, the sum of the coefficients, is unity. One may further note that,in some embodiments, the time-domain impulse response hi_(p) ofscaled-integrator kernel block Hi_(p) 202 is generally bell-shaped,particularly evident for N_(i) greater than or equal to about 3. Inpractice, in some embodiments, w_(p) is set to a value of at least 2samples, since both w_(p)=0 and w_(p)=1 are degenerate cases resultingin all-zero coefficients and a unit impulse for hi_(p), respectively,regardless of N_(i). In some embodiments, impulse responses hi_(p) aresymmetric about their time center regardless of N_(i) or w_(p). Theintrinsic delay of the impulse response hi_(p), equivalent to the timedelay to the time center of hi_(p), is Di_(p)=N_(i)(w_(p)−1)/2 samplesand this delay is integer-valued for N_(i) being even and/or w_(p) beingodd. The z-domain transfer function of the scaled-integral kernel blockis:

${{Hi}_{p}(z)} = \left\lbrack \frac{1 - z^{- w_{p}}}{w_{p}\left( {1 - z^{- 1}} \right)} \right\rbrack^{N_{i}}$where, when evaluated on the unit circle (z=e^(jω)), the frequencyresponse is:

${{Hi}_{p}(\omega)} = {\left\lbrack \frac{1 - {\mathbb{e}}^{{- j}\;\omega\; w_{p}}}{w_{p}\left( {1 - {\mathbb{e}}^{{- j}\;\omega}} \right)} \right\rbrack^{N_{i}} = {\left\lbrack \frac{\sin\left( {\omega\;{w_{p}/2}} \right)}{w_{p}{\sin\left( {\omega/2} \right)}} \right\rbrack^{N_{i}}{\mathbb{e}}^{{- j}\;\omega\;{{N_{i}{({w_{p} - 1})}}/2}}}}$In some embodiments, the amplitude of Hi_(p)(ω) exhibits a classicperiodic-sinc shaped lowpass response having a mainlobe centered at dc(ω=0) with a peak amplitude of unity, sidelobes of generally decreasingamplitude towards Nyquist (ω=π), and amplitude zeros (nulls) atω_(p)=2π·r/w_(p) radians, or f_(p)=Fs·r/w_(p) Hz, where Fs is the samplerate in Hz, and where r=1, 2, . . . N. The lowpass mainlobe passband hasbandwidth inversely proportional to w_(p) and monotonically inverselyrelated to N_(i). With increasing N_(i) the sidelobes becomeprogressively more attenuated and the overall amplitude response becomesprogressively more bell-shaped. The phase response of Hi_(p)(ω), afteraccounting for the linear phase term due to the intrinsic delay Di_(p),is flat and equal to zero radians in the lowpass mainlobe, and continuesto be flat in the sidelobes, alternating between 0 and π radians everyother sidelobe for odd values of N_(i) (i.e., the phase is flat and zeroradians everywhere for even values of N_(i)).

Application of Hd_(p) and Hi_(p) to H_(p)

In some embodiments, component filter H_(p) 106 is composed of thecascade of scaled-derivative kernel block Hd_(p) 201 and scaled-integralkernel block Hi_(p) 202, and the frequency response of component filterH_(p) 106 is the product of the frequency responses of Hd_(p) 201 andHi_(p) 202. In practice, the band-tuning parameters k_(p) and N_(d) ofHd_(p) 201 are tuned to set the center frequency and bandwidth of thefundamental passband, and by consequence, the harmonic passbands, ofcomponent filter H_(p) 106. In some embodiments, the band-tuningparameters w_(p) and N_(i) of Hi_(p) 202 are tuned to set the degree ofsuppression needed, if any, in the harmonic passbands. This is typicallyaccomplished by disposing the width of the mainlobe of Hi_(p) 202 sothat it covers the fundamental passband of Hd_(p) 201, along with thedesired number of successive harmonic passbands, if any, and so that theundesired harmonic passbands reside in the region of the nulls andsidelobes of Hi_(p) 202. If no harmonic suppression is needed, the blockHi_(p) 202 may be eliminated, or equivalently, replaced with a unitimpulse (e.g., by setting w_(p)=1).

In some embodiments, only the fundamental passband is kept, and allharmonic passbands are suppressed without loss of generality (WLOG). Insome embodiments, a degree of optimality in overall harmonic suppressionmay be obtained by placing the first null of Hi_(p) 202 as close aspossible to the peak of the first harmonic passband. In someembodiments, the relation:

$w_{p} \equiv {Q\left\lfloor {\frac{2}{3}k_{p}} \right\rfloor}$where the operator Q[x] quantizes x to an integer, satisfies thisconstraint. The operator Q[•] may take the form of the ceiling, floor,or rounding operation, and, in some embodiments, in order to satisfy therequirements of a multiplierless architecture, may be made to round tothe nearest integer power of 2.

Complete Parallel Filter Bank

In some embodiments, the family of component filters H_(p) 106, p=1 . .. N forms a class of wavelets. The degree of orthogonality among thevarious wavelet scales may be controlled in the frequency domain bydetermination of the degree of overlap between band responses. Overlapmay be reduced by increasing the frequency spacing between theassociated passbands, and/or by increasing N_(d), and/or by generallysuppressing harmonic passbands. Note that in the above examples, theband-tuning parameters k_(p), N_(d), w_(p), and N_(i) were implicitlyheld constant with respect to band index p. These band-tuning parametersmay, in fact, be made individually tunable band-by-band, for example inorder to optimize orthogonality in a particular filter bankimplementation. One may now define the values of delay m_(p) for thedelay blocks D_(p) 107, p=1 . . . N in the parallel filter bank 100 ofFIG. 1. Typically, a delay block D_(p) 107 is used to compensate forintrinsic delays in component filter H_(p) 106 with respect to theremaining of such set of blocks. One may note that the intrinsic delayof component filter H_(p) 106 is Dd_(p)+Di_(p) and is generallydifferent for every component band. Some systems may accommodate thisrelative delay by accounting for each known delay Dd_(p)+Di_(p), forexample in the values of the object timestamps in the context of thecurrent invention. Other systems may require the real-time output ofeach component band to be aligned in time. For such systems, the delayof each delay block D_(p) 107 should be set such that the intrinsicdelays from x to each y_(p) 108 in the bank are substantially the samefor all p=1 . . . N. In some embodiments, this time alignment may beaccomplished with minimum overall delay by having:m _(N)=0m _(p)=(Dd _(N) +Di _(N))−(Dd _(p) +Di _(p)),p=1 . . . N−1where delay block D_(p) 107 is implemented with impulse response:d _(p)(n)=δ(n−m _(p))

Example Cascade Filter Bank

FIG. 5 is a block diagram of an exemplary cascade filter bank 500 ofsome embodiments. This illustrates an example method of decompositionsof some embodiments of the present invention in the form of filterbanks, using a bank of filter sections disposed in cascade form. Anoriginal signal x 501 is applied to the input of the filter bank 500,and a set of N component signals y₁ . . . y_(N) 512 are provided at theoutputs of the N filter sections in filter bank 500. In someembodiments, the filter sections are each comprised of a network ofcomponent filters and two delay elements, with the component filtershaving transfer functions denoted by H₁ . . . H_(N) 508, the first delayelements having transfer functions denoted by D₁₁ . . . D_(N1) 509, andthe second delay elements having transfer functions denoted by D₁₂ . . .D_(N2) 510. The p^(th) filter section 513 of filter bank 500, where p=1. . . N, is thus comprised of component filter H_(p) 507, first delayelement D_(p1) 502, and second delay element D_(p2) 503.

In some embodiments, the input to the p^(th) filter section in the bankis applied to the inputs of both block H_(p) 507 and first delay blockD_(p1) 502. In some embodiments, the output of H_(p) 507 is applied tothe input of second delay block D_(p2) 503, forming p^(th) componentoutput y_(p) 506. In some embodiments, the output of H_(p) 507 is alsosubtracted from the output of D_(p1) 502, forming p^(th) signal g_(p)504, called a “residual” or “feed-forward” output signal associated withthe p^(th) band. The filter sections are then connected such that theinput to the p^(th) filter section is g_(p-1) 505, (i.e., the residualoutput signal of the previous band, for p=2 . . . N). For p=1, the inputof the filter section is the input signal x 501. Hence, in someembodiments, the filter bank features a cascade topology.

In some embodiments, the component filters H_(p) 507 may be implementedaccording to the description of the H_(p) 106 corresponding to the aboveexample parallel filter bank of this invention, with the condition thatthe coefficients of H_(p) 507 be normalized so that the correspondingfrequency response magnitude peaks are unity, and with the conditionthat H_(p) 507 exhibit zero phase in the fundamental passband (afteraccounting for the linear phase term due to the intrinsic delay of H_(p)507). The zero-phase condition may be met by having N_(d) be a multipleof four, or an appropriate multiple of two with corresponding polarityadjustment of the coefficients for zero phase in the passband ofinterest. In some embodiments, delay block D_(p1) 502 would bepreferably assigned delay value m_(p1)=Dd_(p)+Di_(p) as per H_(p) 106,and specifically the intrinsic delay of block H_(p) 507, where delayblock D_(p1) 502 is implemented with impulse response:d _(p1)(n)=δ(n−m _(p1))

With regard to the p^(th) transfer function Tg_(p) defined from g_(p-1)to g_(p), the time alignment between delay element D_(p1) 502 andcomponent filter H_(p) 507 produces cancellation in the frequencyresponse of Tg_(p) at the frequency-response-magnitude peaks of H_(p)507 having zero phase (e.g., the fundamental passband peak). As such,the residual output signal g_(p) contains the information of residualoutput signal g_(p-1), except with the significant frequency content (ifany) associated with component filter H_(p) 507 having beensubstantially removed. (In this sense, original signal x 501 may beconsidered as residual g₀, as it has had no information removed.) Withregard to the p^(th) transfer function Ty_(p) defined from input x 501to output y_(p) 506, this arrangement of cascaded residual outputsignals results in a set of transfer functions Ty_(p), where p=1 . . .N, that are mutually approximately orthogonal.

In some embodiments, by choosing the wavelet scale factors k_(p) so thatthey are monotonically decreasing with increasing p, the residuals g_(p)and the transfer functions Ty_(p) become progressively more lowpass(i.e., have progressively less high-frequency content) due to theinformation-removal property of cascade filter bank 500 as discussedabove. This has the effect of suppressing harmonic passbands present inthe derivative kernel block Hd_(p), reducing or eliminating the need forthe harmonic suppression from the scaled integrator kernel Hi_(p) incomponent filter H_(p) 507. In some embodiments, the final residualoutput signal g_(N) would thus contain substantially lowpass-only,near-DC information, such as baseline artifacts, and particularly noperiodic or qp information, and so would not be used for creation ofobjects in the context of this invention. The lowpass residual outputmay, however, be useful for general signal-processing tasks and so isleft as part of this invention. One may now define the values of delaym_(p2) for the second delay elements D_(p2) 503, p=1 . . . N, in cascadefilter bank 500 of FIG. 5. With regard to p^(th) transfer functionTa_(p), defined from input x 501 to the input of second delay elementD_(p2) 503 for p=1 . . . N, delay block D_(p2) 503 may be used tocompensate for intrinsic delays in Ta_(p) with respect to the remainingof such set of transfer functions. One may note that the intrinsic delayDa_(p) of transfer function Ta_(p) is:

${{Da}_{p} = {\sum\limits_{s = 1}^{p}\; D_{s\; 1}}},{p = {1\ldots\mspace{14mu} N}}$and is generally different for every component filter. As with theparallel filter bank 100 of FIG. 1, some systems may accommodate thisrelative delay by accounting for each known delay Da_(p), for example inthe values of the object timestamps in the context of the currentinvention. Other systems may require the real-time output of eachcomponent band to be aligned in time. For such systems, the delay ofeach second delay element D_(p2) 503 should be set such that theintrinsic delays from original signal x 501 to each component signaly_(p) 506 in filter bank 500 are substantially the same for all p=1 . .. N. In some embodiments, this time alignment may be accomplished withminimum overall delay by having:

m_(N 2) = 0${m_{p\; 2} = {\sum\limits_{s = {p + 1}}^{N}\; D_{s\; 1}}},{p = {{1\mspace{11mu}\ldots\mspace{14mu} N} - 1}}$where delay block D_(p2) 503 is implemented with impulse response:d _(p2)(n)=δ(n−m _(p2))

An important property of the cascade filter bank is that the originalsignal x 501 is perfectly recovered from the sum of the outputs and thefinal lowpass residual, that is:

${{\sum\limits_{p = 1}^{N}\;{y_{p}(n)}} + {g_{N}(n)}} = {x(n)}$

Partial sums of outputs over a subset of p=1 . . . N may also be used todefine wider bands from a combination of narrower ones (“compositebands”), and linear combinations of the outputs of narrower bands maymore generally be used to define composite bands of arbitrary shape, forexample using the band centers of each transfer function Ty_(p) toapproximately define a value of frequency-domain magnitude.

Analytic Filter Banks

FIG. 6 is a block diagram of a section 600 of an analytic filter bankthat can employ any of the block arrangements described above. In theexample shown in FIG. 6, the output of p^(th) filter section, i.e.,component signal y_(p) 601, is processed to produce a substantialapproximation of a corresponding analytic component signal having a realparty y^(re) _(p) 604 and an imaginary party y^(im) _(p) 603. In someembodiments, the component signal y_(p) 601 output from thecorresponding p^(th) filter section, is processed through aHilbert-transformer block Hh_(p) 602 to produce a substantialapproximation of the imaginary part of the analytic component signaly^(im) _(p) 603. In other embodiments, a basic derivative kernel blockKd_(p) 301 (as per FIG. 3) may be used as a particular implementationfor Hilbert-transformer block Hh_(p) 602 to produce a substantialapproximation of the imaginary party y^(im) _(p) 603 of the analyticcomponent signal.

In some embodiments, the component signal y_(p) 601 of the correspondingof p^(th) filter section, is processed through a delay element Dh_(p)605 to form the real part of the analytic component signal y^(re) _(p)604. In some embodiments, delay element Dh_(p) 605 is set for a delaysubstantially equal to the intrinsic delay mh_(p) of Hilbert-transformerblock Hh_(p) 602 over at least the bandwidth of interest for the p^(th)component. This is useful for systems requiring the intrinsic delay fromy_(p) 601 to real party y^(re) _(p) 604 to be the same as that fromy_(p) 601 to imaginary party y^(im) _(p) 603. In some embodiments delayelement Dh_(p) 605 is implemented with the following impulse response:dh _(p)(n)=δ(n−mh _(p))

In some embodiments, basic derivative kernel block Kd_(p) 301 is usedfor Hilbert-transformer block Hh_(p) 602. In this case, the delay ofblock Dh_(p) 605 may be set with mh_(p)=k_(p)/2, equivalent to theintrinsic delay of the basic derivative kernel block Kd_(p) 301. Thisdelay is integer-valued for k_(p) even.

The outputs of p^(th) analytic filter bank 600 may then be used to formthe p^(th) complex analytic signal:y ^(c) _(p) =y ^(re) _(p) jy ^(im) _(p)such that y^(c) _(p) may be generally applied for spectral analysis,instantaneous frequency and amplitude measurement, and relatedapplications well understood and established in the art of analyticsignals and communications systems. In particular, regarding the currentinvention context, in some embodiments, the analytic component signalsmay be used for defining quarter-phase or fractional-phase objects andattributes. With regard to the p^(th) transfer function Ty_(p) definedfrom input x to output y_(p) 601 of any of the above filter banks,Ty_(p) may thus be extended using this example arrangement to analytictransfer functions Ty^(re) _(p), Ty^(im) _(p) and Ty^(c) _(p), andapplied as analytic filters in either the time or frequency domain,again as well understood and established in the art.

In some embodiments the intrinsic delay mh_(p) from component signaly_(p) 601 to imaginary part y^(im) _(p) 603 may depend upon the scale(indexed by p), particularly for Hilbert-transformer block Hh_(p) 602implemented in real-time form using basic derivative kernel block Kd_(p)301. In such cases, the intrinsic delay of transfer function Ty^(im)_(p) (and by extension, where applicable, that of Ty^(re) _(p)) would beadditively increased over the intrinsic delay of transfer functionTy_(p) by the delay mh_(p). As with the above filter-bank examples, somesystems may accommodate the relative delay of the various real andimaginary signal paths of the various bands of the analytic filter bankby accounting for each known delay, for example in the values of theobject timestamps in the context of the current invention. Some systemsmay require the real-time output of each analytic component band to bealigned in time. As is well known, for such systems, the delay of eachdelay block for each component band may be adjusted so that theintrinsic delay from original signal x to each real party y^(re) _(p)and imaginary party y^(im) _(p) in the analytic bank are substantiallythe same for all p=1 . . . N. (See Discrete-Time Signal Processing, byA. V. Oppenheim, R. W. Schafer, and J. R. Buck, Prentice-Hall, 1999.)This may be accomplished in the above examples with minimum overalldelay by adding the delay-adjustment terms:m _(N3)=0m _(p3) =mh _(N) −mh _(p) ,p=1 . . . N−1to the respective delay terms m_(p), p=1 . . . N for the parallel bank,or respectively to m_(p2), p=1 . . . N for the cascade bank.

The above-described development of the filter-bank examples does notstrictly mention the notions of completeness, including overcompletenessand/or undercompleteness, and sampling, including over sampling and/orsub-sampling, as understood and established in the sense of wavelettransform considerations. With regard to completeness, in someembodiments, decomposition need only substantially represent and/orsupport the components in the signal that are meaningful to itsrepresentation in the sense that the objects produced correspond todesirable feature points on the original signal morphology. In the caseof reconstruction of an estimate of the original signal from objects,meaningful components may be those in which the reconstructionsubstantially represents the original signal morphology to somepredetermined degree of accuracy, such as, for example, clinicalequivalence for diagnosis by a physician in the ECG example, or as amore general example, some predetermined maximum allowable amount ofmean-square error. Thus, undercomplete transforms as well asovercomplete transforms may be readily accommodated in some embodiments.

With regard to sampling, some embodiments of the wavelet transformsub-sample (i.e., they decimate) the individual inputs and/or outputs ofthe component bands, taking advantage of the fact that the componentsignals and band filters may carry significantly less informationcontent than the original signal. The resulting sub-sampled filter bankis also called a multirate filter bank. Systems that do not sub-sampleare also called oversampled systems. The current invention may beconsidered a multirate filter bank, in the sense that thefractional-phase objects are sub-samples of their correspondingcomponent signals. However, in this case the sample timings of objectsdepend on characteristics of the component signals and not simply uponsome proportion of the sample period of the original signal. In someembodiments, sub-sampling of one or more of the component bands may takeplace. In addition, the sub-sampled component signals may be carriedforward in the processing system in place of and/or in addition to thenon-sub-sampled component signals. In some embodiments, objects basedupon such sub-sampled component signals may be formed. One may presentan example filter bank employing a single sample rate throughout (i.e.,an oversampled filter bank) WLOG.

As to reconstruction, sub-sampled systems rely on interpolation toreconstruct the component signal at the original sample rate, typicallyusing zero-interpolation by the corresponding decimation factor, andthen filtering the zero-interpolated result using a resynthesis filter,which is, in some embodiments, defined according to well-establishedwavelet transform methods. In some embodiments, interpolation andresynthesis may be employed, utilizing the object stream from eachcomponent to define impulses, or narrow interpolation kernels, on theoriginal sampling grid. In some embodiments, each impulse would beweighted by its corresponding object amplitude, and centered at the timeof the corresponding component peak. Overlap between interpolationkernels, if any, would be summed, sample by sample. The remainingsamples would be set to zero. The resulting sequence would then bepassed through a reconstruction filter for the corresponding componentband, thereby reconstructing the component signal.

In the sense that completeness and sampling fit into the context of aframe in the wavelet transform, frames of any width and height in thetime-frequency plane may thus be considered as part of this invention,provided that they meet the criteria given in this invention for signalrepresentation and/or reconstruction.

Example Objects, Data Structures, Quarter-Phase Representation, andSystems and Methods for Implementing

The output of decomposition is generally a set of component signalshaving a simple morphology, substantially resembling sine waves, thoughnot necessarily purely sinusoidal. In some embodiments, the variouscomponent filters outlined above will be excited to a varying degree byunderlying components in the signal that correlate to the impulseresponse shape of the corresponding component filter, producing acomponent output (e.g., y_(p) 108, y_(p) 506) that is active. Therelative amplitude of an active component will be affected by the degreeof correlation and the overall strength of the signal. The states ofactive or inactive may be discriminated according to amplitude of thecomponent, for example, comparing it to a fixed threshold or adaptivethreshold based upon the total time-local energy of the signal.

Some embodiments provide a method to discretely quantify meaningfulevents in active components, in that these events tend to exhibit strongcorrespondence to desired feature points along the original signal.Active component signals should generally exhibit regular undulationswith readily distinguishable cycles and corresponding cycle features.

FIG. 7A is a waveform 700 illustrating a cycle from one component signalthat is broken up into four quarter-phases. This shows an example of amethod whereby a cycle from one component signal is broken up into fourquarter-phases, labeled as A, B, C, and D. This component signal isbroken up into and, in some embodiments, defined according to certainunique features such as the positive-going and negative-going zerocrossings, and the positive and negative peaks of the component signal.These unique features are shown in a table format in FIG. 7B.

FIG. 7B is a table 751 illustrating the various unique features such asthe positive-going and negative-going zero crossings, and the positiveand negative peaks of the component signal that are analyzed by theinvention

In practice, these unique features such as the zero crossings may bereadily detected by a change in sign of the signal, while the peaks maybe detected by any of various means well known and established in theart, including a corresponding change in sign of the derivative of thesignal. Alternatively, in some embodiments, given the availability ofanalytic signal components, the positive-going and negative-going zerocrossings of the imaginary part of the component signal are used torepresent the positive and negative peaks of the real part and viceversa. The feature points bound the four quarter-phases and thereforedefine the transitions of the component signal between and among thefour quarter-phases A, B, C, and D. In some embodiments, the componentsignal is defined as being “in” a particular quarter-phase at any pointin time that the component signal satisfies the starting and endingcriteria for that quarter-phase. Under those conditions thequarter-phase is defined as “active” for the corresponding component.

These unique features, quarter-phase transitions of the componentsignals tend to correspond strongly to points of interest (i.e., featurepoints) along the original signal, such as, for example, local peaks,valleys, inflections, curves, and changes of shape. In some embodiments,this property is utilized for various types of robust analysis. Oneexample application of this property is that of robust delineation (orsegmentation) of an ECG waveform into the various waves (e.g., P, Q, R,S, T, U, etc.) and segments between these waves, along with the variousapplications of such delineation. This property can be utilized in theanalysis of most any qp waveform.

FIG. 7C describes how four quarter-phases (i.e., Nos. 701, 702, 703,704) of waveform 700 may be characterized simply using two parameters.First, the time duration T4 (i.e., Nos. 705A, 705B, 705C, 705D) of aquarter-phase is the length of time from the beginning to the end of thequarter-phase (i.e., the time between consecutive quarter-phasetransitions of the component signal). Second, the amplitude a (i.e.,Nos. 706A, 706B, 706C, 706D) of a quarter-phase is a measure of thevertical deviation of the quarter-phase over its course. The amplitudeof the quarter-phase is the absolute value of the peak (i.e., themaximum absolute value) of the component signal over the course of thequarter-phase. For quarter-phases A and C, this value is obtained at theend of the quarter-phase, while for quarter-phases B and D, this valueis obtained at the beginning of the quarter-phase. Alternately, in someembodiments, this amplitude may be any amplitude measure well known andestablished in the art, including the mean absolute value or theroot-mean-square value of the component signal taken over the timeduration T4 of the quarter-phase, or a local measure of the magnitude ofthe analytic signal for that component.

In some embodiments, by following the quarter-phase structure, theimaginary part of the analytic signal for a component may beapproximated at any given point by evaluating that component signal at atime T4 to either side from that point. The value used for T4 in thisevaluation may be the most recent value for that component, or it may bea constant derived from a quarter of the period at the center frequencyof the component band.

In some embodiments, T4 may itself be estimated by taking the timebetween that last two zero-crossings or, if appropriate, the current andprior zero crossings and dividing that value by two.

In some embodiments, these parameters (L, a, T4) are collected, where Lis the label of the quarter-phase (i.e., one of A, B, C, or D), and thiscollection is referred to as a “quarter-phase object” (or “object”)O_(ij) occurring at the i^(th) time index for the j^(th) component. Acorresponding notation for the continuous-time case would be O_(j)(t),where t is continuous time. This object representation therefore createsa compact packet of concentrated information about the component signaland hence, the original signal, precisely tied to a point of interestalong the original signal.

In some embodiments, these quarter-phase objects will then berepresented as structures or objects in a computer program. In someembodiments, these objects will have data attributes that correspond tothe parameters (L, a, T4). The representation of data through the use ofobjects is well known in the arts of software engineering and computerscience. (See Data Structures and Other Objects Using C++ 2^(nd)Edition, by M. Main and W. Savitch, Addison-Wesley, 2001.) The use ofthe term object implies a meaning as understood by these arts.

In some embodiments, the absolute timing of an object (i.e., the time ofoccurrence of the object) may be referenced to the beginning time orending time of its associated quarter-phase. However, in a given systemimplementation one reference should be chosen and used consistentlythroughout.

In still other embodiments, over the course of a typical componentsignal, a stream of such objects will be generated, which may be stored,transmitted, and/or analyzed. The index i may be indexed according tosome underlying constant sample rate, in which case i may be consideredas a form of time-stamp for the generated object. In some embodiments,index i would be incremented upon generation of each correspondingobject. In this case, the absolute time of occurrence for a particularO_(ij) may be stored along with the object, or it may be reconstructedby summing the values T4 for that component from some arbitraryreference time.

In some embodiments, the values T4 may be measured, if necessary, witharbitrary resolution using interpolation methods to determine the zerocrossings and/or peaks of the component signals. Interpolation methodsto accomplish this are well known and established in the art, includingmethods such as polynomial fitting and cubic splines in a neighborhoodof the zero crossing of the curve or a zero crossing of the derivativeof the curve.

Also depicted in FIG. 7C is the inherent symmetry of the quarter-phaserepresentation along the amplitude and time axes. This symmetry isutilized in the signal-compression signal-concentration aspect of theinvention. In some embodiments, it can be noted that for a given cycle,the simple amplitude measure is the same for quarter-phases A and B, andfor quarter-phases C and D. This offers some further compactness ofinformation in the object representation, in that the value of a needonly to be evaluated once for the A-B transition, and once for the C-Dtransition.

While the component signal in FIG. 7A appears to be sinusoidal, thecomponent signal only need be roughly sinusoidal, to the extent that thequarter-phases may be identified unambiguously. In some embodiments, acomponent signal whose unwrapped analytic phase is roughly monotonicover time would be a reasonable candidate for this method.

FIG. 7D shows a method 752 for breaking up into quarter-phases thecycles of two component signals, the time ranges over which the phaselabels are coincidentally stable (quarter-phases are active), and thestates defined by vectors of those labels, indicated here with labelsAa, Ab, Ac, Bc, Bd, Cd, Ca, Cb, Db, Dc, Dd, etc. In some embodiments,method 752 processes two component waves including a lower-frequencycomponent wave 707, and a higher-frequency component wave 712. Thelower-frequency component wave 707 includes, in some embodiments, ofquarter-phases A 708, B 709, C 710, and D 711. Similarly, in someembodiments, the higher-frequency component wave 712 includesquarter-phases a 713 and b 714, c 715, d 716, with the cycle repeatingto a 717, b 718, c 719 and d 720. Also depicted in FIG. 7D are activestates referenced by various state labels. For example, state label Aa721 refers to the active state corresponding to a portion of thelower-frequency component wave 707, and higher-frequency component wave712. Similarly, Ab 722, Ac 723, Bc 724, Bd 725, Cd 726, Ca 727, Cb 728,Db 729 (depicted but not labeled), Dc 730, and Dd 731 all depict variousactive states corresponding to portions of waves 707 and 712. In someembodiments, these active states continue so long as there is a signalto be processed.

In some embodiments, the four points of transition betweenquarter-phases also correspond to the quarter-wave points along theforward transition of the analytic phase of the component (i.e., wherethe analytic phase substantially equals 0, 90, 180 and 270 degrees, or0, π/2, π, and 3π/2 radians, respectively). From this perspective,further wave subdivisions are possible, say, defining fractional-phaseregions bounded by N fractional phase increments nπ/N, n=0, 1, . . . ,N−1 along with their associated objects, labels and parameters. In someembodiments, using N=16, quarter-phase A could be subdivided into fourfractional-phase regions A1, A2, A3, and A4, and likewise forquarter-phases B, C, and D, resulting in sixteen fractional-phaseregions (i.e., one-sixteenth-phase regions). The particular region thecomponent signal is residing in at any given time would be determined bycomparing the analytic phase to the bounding phase values. As beforewith parameter T4, a parameter TN would be the time length betweenfractional-phase transitions into and out of the correspondingfractional phase. The amplitude parameter a of the corresponding objectmay be determined as before, for example by the maximum value, meanvalue, rms value, or related amplitude measure over the fractionalphase, with the added ability to evaluate these based upon acomplex-amplitude measure (i.e., the analytic amplitude).

FIG. 7E is a matrix 753 containing the sixteen possible quarter-phaseregions (states) of a method 700, for two component waves.

In some embodiments, for a set of Nc components, for quarter-phaseobject resolution the total number of potential states is 4^(Nc). Forthe fractional-phase case with total possible fractional phases F (suchthat for quarter-phase, F=4), the total number of potential states isthus F^(Nc). For example, with quarter-phases, the system of twocomponents may take on any of the 16 possible states (4²), dependingupon the relative timing and phase offset of the two components. Again,a system of three components may take on any of the 64 possible states(4³). This may extended to the case of any number of components andfractional phases.

In some embodiments, these finer fractional-phase transition points,fractional-phase regions and resulting objects may be suitable forcertain applications requiring a finer underlying phase resolution percomponent, such as situations where the component signal substantiallydeviates from a sinusoid. However, it can be noted that resolving theanalytic phase, in some embodiments, requires evaluation orapproximation of the inverse tangent function, which may be prohibitivein certain applications. Further, it can be noted that, in someembodiments, such finer resolution may also be obtained by simply addingand utilizing an appropriate higher-frequency component and itsassociated quarter-phase objects. Thus, the fractional-phaserepresentation is provided as an embodiment of this current invention,and a direct extension of the quarter-phase representation.

One may further note that, in some embodiments, the complete analyticsignal may be utilized. In some embodiments, the fractional phases andtransitions of the imaginary part of the analytic signal may be detectedand measured, thereby generating objects for the imaginary part insubstantially the same manner as already described for the real part.Additionally, timing and amplitude information from the real-partobjects may be used for the imaginary-part objects, and vice versa. Forexample, the real and imaginary parts of the amplitudes may be recordedat all fractional-phase transitions for either or both real andimaginary parts, to establish a moving measure of local energy in thecomponent. Adding the imaginary-part information serves to increase theamount of objects and/or the amount of information per object. This inturn increases the object storage and/or transmission requirements, butcan be useful for processing steps involving the objects.

In some embodiments, a special case exists having quarter-phases basedupon an analytic component. In this case, zero crossings from both thereal part and the imaginary part of the component are used to define thequarter-phase regions, and the complex amplitude information at thesezero crossings is used to establish an amplitude measure. This case hasparticular applicability to multiplierless architectures for creation ofquarter-phase objects.

In some embodiments, if the amplitude measure of an object falls below acertain threshold, the object could be flagged, or a special objectcould be generated, to indicate the absence of the component, whereuponthe component and/or object are considered “quiescent.” This is usefulfor analysis of signals having relatively long quiescent periods in someor all components, such as, for example, ECG signals in thehigher-frequency components centered roughly 20 Hz and above.

As the original signal varies or undulates over time, various objectsare generated from the corresponding components, and the signal takes onvarious states, acquiring a new state with each object generated. Thesequence of states may then be analyzed as a collection forcharacterizing the original signal. For example, a purely periodicoriginal signal would produce a perfectly repeating pattern of states.Provided that the fundamental period falls within the passband of one ofthe components, the substantially lowest-frequency component that isactive would produce the same object, for example, an object with the“A₁” label, once per fundamental period. If needed, more precision intime would be afforded in this example by detecting particular labelsfor higher-frequency components synchronous with the “A₁” label.Quasi-periodic patterns may also be followed by identifying local cyclesof repetition among the states and objects. For example, a certainsequence of states and/or object labels may be identified with anoriginal signal associated with a certain condition. The entire sequencemay be given a label according to that condition, and stored andprocessed simply according to this predetermined label. Next isdescribed how to extend this general idea of linking objects and states,and how to utilize these links.

Object Links, State Links

FIG. 8A provides a description of a data structure 800 that, in someembodiments, is contained on a computer-readable medium. This objectcontains some and or all of the following attributes:

-   -   Object ID 801—Some unique identifying code or value that        facilitates retrieval of each object.    -   T4 value 802—the value of T4 in some useful unit, such as        discrete and/or fractional samples (i.e., related to a constant        sample rate for the original signal), or seconds.    -   Phase label 803—a quarter-phase object denoted by the letter A,        B, C, or D.    -   Atomic period Value 804—(AP) An object-local estimate of the        period of the component. In some embodiments, T4 multiplied by        4.    -   Amplitude measure 805—the height of the wave from some zero        crossing, amplitude is denoted by a.    -   Total time elapsed 806—from some “origin” or “reference” time.    -   Relative amplitude value 807—relative amplitude (K_(A)) of        object with respect to some meaningful reference, such as the        local amplitude measure of the original signal (amplitude        measure as defined previously).    -   Relative T4 value 808—relative T4 (K_(T)) of object with respect        to some meaningful reference, such as the latest T4 of the        active component with the longest period (i.e., the longest        currently active T4).        This list of attributes is by no means complete and, in some        embodiments, additional attributes such as a component ID        containing data relating to the identity of the component from        which the object originates may be desirable. Further desirable        attributes might include a signal ID to identify the signal upon        which the object is based.

In still other embodiments, an object will contain a single field valueand a single link (i.e., memory address) linking this single-fieldobject to another object. For example, a first such single-field objectmight contain an Object ID field and a link that links the firstsingle-field object to a second single-field object, which contains onlya single T4 value and a link that links the second single-field objectto a third single-field object, which contains a phase label and a linkthat links the third single-field object to yet another single-fieldobject and so on, each single-field object linking to another until someend-of-link. In some embodiments, these objects may contain two datafields (e.g., one for the Object ID and T4 value) and a single link. Insome embodiments, these objects may contain three data fields (e.g., onefor the Object ID, T4 value, and phase label) and a link to otherobjects. In some embodiments, these objects might contain four datafields (e.g., one for the Object ID, T4 value, phase label, and atomicperiod) and a link to another object. In some embodiments, these objectsmight contain five data fields (e.g., one each for the Object ID, T4value, phase label, atomic period, and amplitude measure) and a link toanother object. In some embodiments, these objects might contain sixdata fields (e.g., one each for the Object ID, T4 value, phase label,atomic period, amplitude measure, and total time elapsed) and a link toanother object. In some embodiments, these objects might contain sevendata fields (e.g., one each for the Object ID, T4 value, phase label,atomic period, amplitude measure, total time elapsed and relativeamplitude value) and a link to another object.

FIG. 8B describes a data structure 851 having various links to otherobjects that a particular object might have in some embodiments. Linksare memory addresses as is commonly understood in the art of computerscience and software engineering. (See Java How to Program 3^(rd)Edition, by H. M. Deitel & P. J. Deitel, Prentice Hall, 1999.) In someembodiments, these are the memory addresses of objects. In someembodiments, the following links are associated with the objectsdescribed herein:

-   -   Left object 809—The previous object, of the same phase label,        generated from this same component.    -   Right object 810—The next object, of the same phase label,        generated from this same component.    -   Left harmonic 811—A measure of cyclic regularity leading up to        the current object. A comparison of left local period by label        (LLPL) to either of left local period by time (LLPT) or AP.        Comparison may for example be a ratio (such as LLPL/LLPT or        LLPL/AP) or a difference (such as LLPL-LLPT or LLPL-AP). Ratios        would be compared to unity, and differences compared to zero.        Over-unity ratios or positive differences would be indicative of        non-cyclic transients resulting in fewer fractional-phase        transitions than would be expected in the case of cyclic        regularity. Under-unity ratios or negative differences would be        indicative of non-cyclic transients resulting in more        fractional-phase transitions than would be expected in the case        of cyclic regularity.    -   Right harmonic 812—A measure of cyclic regularity following the        current object. Same as left harmonic, except using right local        period by label (RLPL) and right local period by time (RLPT) in        place of left local period label and left local period time,        respectively.    -   Left neighbor 813—The previous object generated from this same        component, (i.e., O(i−1)j).    -   Right neighbor 814—The next object generated from this same        component, (i.e., O(i+1)j).    -   Lower neighbors 815—The object or objects that are active at        this link's generation and corresponding to (i.e., generated        from) lower frequency components.    -   Upper neighbors 816—The object or objects that are active at        this link's generation and corresponding to (i.e., generated        from) higher frequency components.        In some embodiments, these objects are organized as a list,        stack, queue, tree, heap, hash table or some other structure        known in the art. (See Algorithms in C++ 3^(rd) Edition, by        Robert Sedgewick, Addison-Wesley, 1998.) The determination of        the exact manner in which these objects would be organized would        be implementation-specific, and hence left up to the particular        software developer's efficiency, and speed requirements.

In still other embodiments, an object will contain a single field valueand a single link (i.e., memory address) linking this single-fieldobject to another object. For example, such an object might contain aleft object field and a link linking this object to a secondsingle-field object, which in turn contains only a single right objectvalue and a link that links the second single-field object to anothersingle-field object, which in turn contains a left harmonic and a linkthat links to yet another single-field object, and so on (i.e., a linkedlist of single-field objects). In some embodiments, these objects maycontain two data fields (e.g., left object and right object) and asingle link. In some embodiments, these objects may contain three datafields (e.g., left object, right object and left harmonic) and a link toother objects. In some embodiments, these objects might contain fourdata fields (e.g., left object, right object, left harmonic, and rightharmonic) and a link to another object. In some embodiments, theseobjects might contain five data fields (e.g., left object, right object,left harmonic, right harmonic, and left neighbor) and a link to anotherobject. In some embodiments, these objects might contain six data fields(e.g., left object, right object, left harmonic, right harmonic, leftneighbor, right neighbor, and lower neighbors) and a link to anotherobject. Thus, the linked lists may contain like-length objects allhaving the same number of data fields, or different-length objectshaving different numbers of data fields.

In some embodiments, the various data-containing attributes and fieldswould be filled as objects are generated and information becomesavailable. Information pertaining to objects not yet generated would befilled in as those objects are generated, or at some suitable time afterthose objects are generated. Because objects from another component maybe generated while an object from one component is still active, it ispossible that the links to upper neighbors and lower neighbors mayrequire a list of appropriate links to handle the multiplicity of objectpairings. In some embodiments, the length of such lists of links isexpected to be on the order of the ratio of the periods of theassociated paired components, and so should be readily manageable.

Horizontal Linking

In some embodiments, an object has two different types of links.Horizontal links deal with timing relationships and order of occurrenceof objects and/or attributes of objects all arising from a certaincomponent. Link types 809, 810, 813, and 814 fall into this category.Vertical links deal with relationships between objects arising fromdifferent components, and so link types 815, 816 fall into thiscategory.

Horizontal links are useful for arriving at measures of the localperiodic structure of component signals and, by extension, that of theoriginal signal. In some embodiments, such measures arise from theanalysis of object attributes in the context of horizontal links. Onemay call such measures “linked attributes”.

FIG. 8C depicts a data structure 852, which, in some embodiments,contains linked attributes such as:

-   -   Left local period by label (LLPL) 817—The time difference        between this object and the previous object from the same        component having the same label as this object.    -   Right local period by label (RLPL) 818—The time difference        between this object and the next object from the same component        having the same label as this object.    -   Left local period by time (LLPT) 819—The sum of T4 for this        object and the past three non-quiescent objects from the same        component. More generally, the sum of T4 for this object and the        past N_(T) non-quiescent objects from the same component,        normalized by the factor 4/N_(T).    -   Right local period by time (RLPT) 820—Same as left local period        time except using T4 for this object and the next N_(T)        non-quiescent objects from the same component.    -   Cyclic regularity 821—One may expect that under steady-state        conditions, successive objects generated from a given component        would have labels following a progressive sequence ( . . . ,        A,B,C,D,A, . . . ). In some embodiments, the repeating pattern        could start with any of the labels. Changes in this pattern        would indicate a deviation from steady state, (i.e., a transient        condition, in the associated component band, and may be        appropriately flagged as such with this attribute). If X is        defined as the quiescent label, then transitions to and from the        X label in the object sequence would signify the special case of        that component becoming inactive or active, respectively, and        may also be appropriately flagged as such with this attribute.    -   Amplitude modulation 822—Difference or ratio of amplitude        measure a of this object and that of neighboring object (may be        right or left).        Further examples involve statistics on attributes, such as the        mean, variance, trend analysis, estimation of probability        density and distribution, or time-series models operating on        such indexed sets of attributes. Typically, linked attributes        deal with measures that collect sets of attributes from two or        more related (i.e., linked) objects, which are applicable at the        time of the current object. In some embodiments, these        measurements are associated as attributes with an object, while        in other embodiments these measurements may be derived from two        or more objects, but the actual measurements and resulting        values (e.g., linked attribute values reference numbers 817,        818, 819, 820, 821, 822) themselves are stored elsewhere (e.g.,        in a separate data structure, or variable).

In still other embodiments, an object will contain a single field valueand a single link (i.e., memory address) linking this single-fieldobject to another object. For example, a first such single-field objectmight contain an LLPL field and a link that links the first object to asecond single-field object, which in turn contains only a single RLPLvalue and a link that links to yet another single-field object, which inturn contains an LLPT field and a link that links to still anothersingle-field object and so on. In some embodiments, these objects maycontain two data fields (e.g., LLPL, and RLPL) and a single link. Insome embodiments, these objects may contain three data fields (e.g.,LLPL, RLPL, and LLPT) and a link to other objects. In some embodiments,these objects might contain four data fields (e.g., LLPL, RLPL, LLPT andRLPT) and a link to another object.

Vertical Linking

Vertically linked objects (VLO) are defined as those objects that arecollectively, simultaneously active and linked vertically. In someembodiments, by using the attributes contained in objects and/or byperforming calculations using these attributes it is possible togenerate or recreate various representations of the component wavesignals that served as the basis for the VLO. One such representation isthe state matrix and/or the state-transition matrix. Such state-relatedmatrices are well understood in the art, for example, in the area ofHidden Markov Models.

In some embodiments, when representing VLO in a transition matrix it isimportant to define “a state” as consisting of a vector of objectsderived from one or more original signals, and a “state space” as thediscrete vector space indexed along each unique dimension by objectlabels corresponding to a particular component. It is then possible torefer to a trajectory of states as traversing such a state space.

FIG. 9A is a state-transition matrix 900 of the available states for asystem of two components. In this example, the available statesrepresent two components with quarter-phase objects, for a total of 16possible states. In the table are shown a few examples of statetrajectories drawn as arrows to the next state along the trajectory. Theillustrated example depicts a series of transitions fromAa→Ab→Ac→Bc→Bd→Cd→Ca→Cb→Db→Dc→Dd. FIG. 9C is a simplifiedstate-transition matrix 902 of the available states for a system of twocomponents. The illustrated example depicts a series of transitions fromAa→Ba→Bb→Bc→Cc→Dd.

In FIG. 9A, the object labels X and x are given to signify a “don'tcare” condition, which could arise from any arbitrary criteria, forexample, due to objects having amplitude measures below a certainthreshold (quiescent objects). As shown in the table, the don't-carecondition is associated with multiple states, which may be takencollectively as a “super state” that is useful for simplifying the stateand/or state-transition analysis.

FIG. 9B represents a state-transition matrix 951, again in table form.This matrix lists all possible states along each dimension, with thestarting state for a given transition along one dimension and the endingstate for the transition along the other dimension. In this example, twocomponents with quarter-phase objects results in a 16×16 matrix. On agiven state transition, an appropriate entry in the table/matrix isflagged or accumulated. The “ones” in the matrix given in the figurerepresent the state transitions for the associated state trajectorygiven in the figure below the table. While not shown, the table and/ormatrix may be readily extended to include entries corresponding totransitions to and/or from a “don't care” state (or super state). FIG.9D represents a simplified state-transition matrix 952, again in tableform. The illustrated example in FIG. 9D depicts a transition fromAa→Ab→Bc→Bd→Ca.

In these given example tables or state-transition matrices (i.e., FIG.9A and FIG. 9B), only two components with quarter-phase objects areshown for the sake of clarity. Of course, the invention is not limitedto only two components with quarter-phase objects. In actuality, andthroughout this invention, an arbitrary number of components and/orfractional phases may be used.

As stated above, in some embodiments, a periodic or qp original signalwill produce a repeating (or near-repeating) pattern of states. Tracingout the sequence of states with dots and arrows on the table would tendto produce a signature pattern discernable to the human eye. Once such apattern is understood, deviations from such a pattern, even relativelysubtle ones, would also be discernable. Simple sequence detectors may beconstructed to match a particular expected sequence of states, orexpected set of such sequences, particularly cyclic ones originating(and/or ending) at a particular state or fundamental component object(i.e., fundamental in the sense of the lowest-frequency activecomponent). The object/state representation affords the application of avariety of ad hoc methods for identifying morphologic features orsequences of such features. Any of the object data could be utilized inconjunction with state-pattern data to enhance the robustness of thepattern recognition. For example, the vector of object amplitudesassociated with a particular state could be used with a thresholdcondition to qualify a certain state or pattern identification.

Methods of Interpreting and Analyzing Data: Hidden Markov Models andNeural Networks

Methods for automatically training on and identifying such patterns andpattern deviations in state space are well understood and established inthe art, such as Neural Networks (NN) and Hidden Markov Models (HMM).(See Neural Networks: A Comprehensive Foundation, S. Haykin, PrenticeHall, 1999; Learning Hidden Markov Model Structure for InformationExtraction, by Kristie Seymore, Andrew McCallum, and Roni Rosenfeld,American Association for Artificial Intelligence 99, Workshop on MachineLearning for Information Extraction, 1999.) In any case, a trainingdataset would be presented to the system, for example, in the form oforiginal signals representative of the condition(s) to be modeled,thereby producing a corresponding repository of objects and states. Inthe case of training with supervised learning algorithms, the objectrepository would be labeled according to the condition(s). In the caseof training with unsupervised learning, no condition labels would beprovided and models would be formed by identifying emergent clusters,such as for example with Self-Organizing Maps and Generative TopographicMaps.

In some embodiments, training is established, in the NN example, byconverging on a set of weights that correspond to a particular statetrajectory pattern (or cluster of trajectory patterns that are “close”in state-space). In the HMM example, training is established by buildingstatistics on states and transitions between states, such as estimatingthe probability of occurrence of a particular state, and the conditionalprobability of the transition from one particular state to the next. Thestate and state-transition matrices may provide the framework for thesestatistics. While exercising the training dataset, each time a certainstate is encountered, the corresponding element of the state matrixwould be incremented. The length of time spent in a particular state mayalso be accumulated, and/or the time from the previous similartransition may be recorded. Accordingly, each time a certain statetransition is encountered, the corresponding element of the state matrixwould also be incremented. If necessary, these accumulated values may besplit into separate accumulation bins, one for each condition label. Thestatistics can be derived by normalizing by the appropriate count, forexample the total number of trials, the total number of trials for acertain condition, the total number of state transitions, and/or thetotal number of state transitions for a certain trial.

Regardless of the particular method, a test trajectory may be comparedfor degree-of-match to a trained trajectory pattern or set of patterns.As is known in the art, the degree-of-match measure may be based upon adistance measure in state space, or comparison of the probability oftracing that particular path based upon accumulated statistics.

In some embodiments, the structure of objects, states, and state spacein this invention provides a solid framework for building up statemodels such as those found in HMM. Traditionally, given an originalsignal, translating points along this signal into useful states forbuilding an HMM has been difficult. Such translation has been typicallybased on ad hoc methods of delineation of the original signal waveform.The method of this invention provides a natural mapping between discretestates and significant points in the original signal.

For the ECG example, such networks and/or models may be trained usingnominal (or normal) data, and then tested with data that may be normalor correspond to a pathologic condition such as ischemia or arrhythmia.The match or absence of match would act as a discriminator of normal andabnormal conditions. Further, training on a set of one or more differentabnormal conditions would allow distinguishing among them as well.

State Space with Attributes

Each state naturally carries all of the underlying informationassociated with the VLO from which the state is comprised. Thus, at eachstate, all of the attributes, links, and linked attributes including theVLO are available for use. For example, a particular state trajectory orpath may map out an ordered set of states {S_(k), k=1 . . . N_(S)},where N_(S) is the total number of states in the path. One may thusconstruct an N_(O)×N_(S) matrix A of a particular attribute, with thek^(th) column being the N_(O) values of the attribute for the objectsincluding the state S_(k). This matrix may then be applied to anaugmented HMM or NN for training and testing purposes. One exampleembodiment would train an HMM directly on states and state transitionsonly, while training an NN on corresponding attribute matrices for eachtraining trajectory of the HMM. Another example embodiment would useonly the information in the attribute matrices, for example to directlytrain an NN. Such augmented or alternate training could be advantageousin identification or discrimination applications that are more sensitiveto information contained in the ordered attributes.

Note that all embodiments may also use more than one attribute in thetraining and testing, whereupon multiple attribute matrices would beconstructed and utilized. One may add to the list of possible attributesthe time spent in a particular state, which may be expressed as anabsolute time or a percentage of the time for the whole statetrajectory, and/or the period or relative frequency of a particularstate transition.

State Space with Time Context

A state trajectory may be characterized in some cases by certain statesthat are inhabited a relatively long time, connected by intermediarystates that are inhabited for a relatively short amount of time.Similarly, a state trajectory will generally move more slowly (changeless often) along some object dimensions, and at a faster rate alongothers, according to the fundamental frequencies of the associatedcomponent bands. One may thus consider states in their time-context, andso reject states, and/or transitions to and from such states, thatrepresent relatively small time contributions to the state trajectory.This can serve to reduce excess complexity in state trajectories due torelatively rapid chatter between states.

Reconstruction of Base Wave Form

In some embodiments, the base waveform is substantially reconstructedfrom a linear combination of its associated components. A particular setof quarter-phase objects and their associated attributes are used toreconstruct each component wave, and so in turn each reconstructedcomponent wave is used to reconstruct the base waveform. As describedabove, in some embodiments, a quarter-phase object contains theattributes of amplitude (a), time (T4), and an associated label (e.g.,A, B, C, or D). In some embodiments, there is an object ID associatedwith each object. If, for example, one considers four objects (A, a₁,T₁), (B, a₂, T₂), (C, a₃, T₃), and (D, a₄, T₄), one can reconstruct acomponent waveform along time variable t, over an interval from 0 to T,such that:

-   -   A a₁, sin(Π/2·t/T₁)    -   B a₂ cos(Π/2·t/T₂)    -   C—a₃, sin(Π/2·t/T₃)    -   D—a₄ cos(Π/2·t/T₄)

Using the above values, one cycle of the original signal of thecomponent form is estimated. The time adjustment among reconstructedcomponents is maintained by sampling time and applying all componentreconstructions with appropriate time alignments, deriving an absolutetime reference for each component from an accumulation of the T4 values,or using the “total time elapsed” attribute if available to establishabsolute timing. The reconstructed components provide the ability toreconstruct the base quasi-periodic waveform by a linear combination ofthese reconstructed components.

Reconstruction and Data Compression, Storage and Transmission

In some embodiments, higher-frequency components will generate moreobjects within a local time period. For example, the ECG wave will onlybe generated substantially in the area of the QRS complex over arelatively narrow range of time, as a percentage of time per beat.However, other high-frequency components may be discarded to the degreethat the loss of high-frequency information can be tolerated in thereconstruction. Components may be discarded based upon redundancy of theobject and the data stored therein, or other features that render thedata unremarkable relative to other data sets contained in other objectsand components. In some embodiments, a determination of whether or notto discard a component may be based upon a data sampling of theseobjects and components. The ability to discard some high-frequencycomponents has a number of advantages. First, it allows for less storagespace to be used to store the objects. Next, the existence of fewerobjects means quicker retrieval times of the requisite data used inreconstruction. Moreover, the existence of fewer objects means that lessprocessor cycles need to be devoted to a particular data set. Once aredundant data is discarded the remaining data may then, in someembodiments, be transferred in some type of compressed format know inthe art. (See Data Compression 3^(rd) Edition, by David Salomon,Springer, 2004.)

A Software Implementation of the Above Method, Data Structure, and State

One example implementation if the present invention is reflected in thebelow-described calling tree and the associated functions written inpseudo code appearing at each level of the calling tree. For the purposeof brevity and conciseness, below we will provide pseudo-code examplespertaining to the generating of a filter bank (i.e., the GenerateFilterBank node and associated child nodes), and pseudo-code relating to thegeneration of quarter-phase representations in object form (i.e., theGetState node and associated child nodes). The calling tree is describedby the cells and by the successive top-down connections between cells inthe following table:

System_Driver Get State Time Freq. Analysis Generate Filter Bank LoadData Get Objects Linear Phase Filter Returns Filter Band Get Data Findqp Hilbert Multiple Find Zero Transform convolve crossing

One knowledgeable in the art would be able to use this calling tree asan architectural blueprint to implement some embodiments of the presentinvention.

Examples of a System Driver and Methods for Implementing

An example pseudo-code representation of a System_Driver function for asoftware implementation of the present invention is described below.Central to this System_Driver function is a GenerateFilter Bank function(“genFilterBank”), in conjunction with a Time-Frequency Analysisfunction (“tfAn”), and a GetState (“getState”) function. ThegenFilterBank implements the analytic wavelets for the above-describedParallel-Form Kovtun-Ricci Wavelet Transform, while the Time-FrequencyAnalysis performs the transform in block form using the precomputedwavelets from genFilterBank. The getState implements the quarter-phaseobject and state representation described above. The pseudo-coderepresentation for the System_Driver function is as follows:

System_Driver ( ); // load data signal ECG into some type of datastructure [data,Fs] = loadECG; // call and generate a parallel filterbank [cH,D,kb] = genFilterBank; // determine the number of filter bandsNb = length(cH); // decompose signal using filter bank, providingresulting components as a matrix X = tfAn(data,cH); // display plot dataand sum of components that approximate the dataplot(real(sum(X′)),‘Linewidth’,2); hold on; plot(imag(sum(X′)),‘:’,‘Linewidth’,2); plot(data); xlim([0 2500]) // setting range of x-axis //captioning for graph legend(‘real’,’imag’,’data’) xlabel(‘time (msec)’)// call find quarter-phase objects and states in component signals[S,cS,sObjStrm,sObj] = getState(X); // determine number of quarter-phaseobjects found Nobj = length(sObjStrm); // plot components withassociated active quarter-phases marked for i = 1:Nb {  subplot(Nb,1,i);plot(real(X(:,i)));  plot(imag(X(:,i)),‘--’);  iact = find(sObj(i).act); inact = find(~sObj(i).act); plot(sObj(i).iqp(iact),real(X(sObj(i).iqp(iact),i)),‘.’); plot(sObj(i).iqp(inact),real(X(sObj(i).iqp(inact),i)),‘x’);  xlim([02500]); // setting range of x-axis  // captioning for graph ylabel([num2str(round(Fs/(2*kb(i)))),‘ Hz’]); } xlabel(‘time (msec)’)// plot data with quarter-phases marked xr = real(sum(X′)); // the sumof the real component values to be plotted plot(xr); hold on for i =1:Nobj {  if sObjStrm(i).act  {  plot(sObjStrm(i).iqp,xr(sObjStrm(i).iqp),‘.’) }} xlim([0 2500]) //setting range of x-axis // captioning for graph title(‘ECG data andactive qp object points marked’) xlabel(‘time (msec)’) // number ofstates Nstate = length(S); // plot zoomed data with qp's marked andstate labels texted // setting ranges of plotting istrt = 250; iend =420; plot(istrt:iend,xr(istrt:iend)); hold on for i = 1:Nstate {  ifprod(S(i,:)) // activity test (all components active to make mark)  {  iObj = cS{i}; // indices back to object stream   iqp =sObjStrm(iObj(1)).iqp; // resolve time index   if iqp > istrt & iqp <iend  // restrict markers to plotted range   {    plot(iqp,xr(iqp),‘.’)// mark state location on data    for j = 1:Nb  // place state labelvertically on    {     plot_text(iqp,xr(iqp)−0.1*j,num2str(S(i,j))) }}}}axis([istrt iend −1 2]) // setting range of axes // captioning for graphtitle(‘ECG data, active qp obj points, and state labels’) xlabel(‘time(msec)’)

One knowledgeable in the art would be able to implement this pseudo-coderepresentation of the System_Driver function and those functionsdescribed below on a general-purpose computer using an object-orientedprogramming language such as C++, C#™, Java™ or Delphi™. Similarly, oneknowledgeable in the art would be able to implement this pseudo code ona general-purpose computer using a structured programming language suchas Matlab™ or C. Alternatively, one knowledgeable in the art coulddesign an embedded circuit/hardware implementation of this pseudo-coderepresentation.

Example Filter Bank and Methods for Implementing

As outlined above in the calling tree, the System_Driver function, insome embodiments, calls a Generate Filter Bank function(“genFilterBank”). Essential to this genFilterBank function is a FilterBand function (“difilter”) to create individual Kovtun-Ricci waveletsand a Returns Hilbert Transform function (“hilbananorm”) to convert realwavelets into normalized analytic form using a Hilbert transform coupledwith normalization in the frequency domain. The pseudo-coderepresentation of the genFilterBank is as follows:

genFilterBank( cH, D, kb) // Functions parameters/arguments // cH =array of analytic wavelet coefficients // D = array of intrinsic delays// kb = array of wavelet scaling factors (relative to Nyquist) Fs =1000; // sample rate (Hz) f0 = 1; // base center freq. (Hz) fc =f0*2.{circumflex over ( )}[0:4]; // freq. centers kb =round(Fs./(2*fc)); // set of scales Nb = length(kb); // number of bands// wavelet parameters Nd = 4; // derivative order Ni = 4; // integratororder // create bank cH = cell(1,Nb); // initializes cells/arrays D =zeros(1,Nb); for i = 1:Nb // generate set of filter responses {  k =kb(i); // derivative scale  w = ceil(2*k/3); // integral scale:“optimal” upper harmonic  [h,D(i)] = difilter(1,k,Nd,w,Ni);  cH{i} =hilbananorm(h)’; }

Called within this genFilterBank function are the difilter andhilbananorm functions. The difilter, among other things, returns an “h”value from the Parallel-Form Kovtun-Ricci Wavelet Transform representinga real wavelet, or component filter impulse response. This wavelet isthen passed to the hilbananorm function implementing a Hilberttransformer. The following pseudo-code implements the difilter:

difilter(x,k,Nd,w,Ni) // Parameters/arguments // x = filter input // k =derivative time-scaling factor // Nd = order of derivative kernel //Optional integrator parameters (must be passed together): // w =integrator time-scaling factor // Ni = order of integrator kernel //compute intrinsic delay from parameters, in samples    D = Nd*k/2 +Ni*(w−1)/2; // composite derivative and widener    kd =[1,zeros(1,k−1),−1]/2; // Scaled derivative kernel    y =mconv(x,kd,Nd); // Nd-order derivative operator    // Scaled integratorkernel    ki = ones(1,w)/w; // Scaled integrator kernel    y =mconv(y,ki,Ni); // Ni-order integrator operator

Once difilter is implemented, a filter output value or “y” value andintrinsic delay value or “D” stored in a data structure, such as anarray, are returned. Function difilter implements an input-output model,such that an input signal “x” is passed in to produce filtered output“y” according to wavelet parameters k, Nd, w, and Ni. On callingdifilter, passing in a “1” for “x”, as does genFilterBank, thus returnsin output “y” the impulse response of the filter, or the wavelet itself.This output is returned into “h” in genFilterBank, then passed to thehilbananorm function, the pseudo-code for which is described as follows:

hilbananorm(hr,domain) // Parameters/arguments // hr = real impulseresponse // domain = domain of normalization, ‘freq’ = default, or‘time’ Na = 2{circumflex over ( )}ceil(log2(length(hr))); // zero-pad toefficient number of points Na = max([Na, 8192]); // ensure a minimumfrequency resolution H = fft(hr,Na); // fft H(Na/2+1:end) = 0; // zeroupper half-spectrum    hc = 2*ifft(H/max(abs(H))); // normalize and ifft   hc = conj(hc(1:length(hr))); // take conj, set back to originallength

In some embodiments, a complex analytic form signal or impulse response“hc” of input signal or impulse response “hr” is returned. In thisexample, value “hc” is the analytic form of the wavelet. This wavelet isthen passed back up to the genFilterBank function and becomes one memberof the cell array “cH” (array of wavelets), and then ultimately up tothe System_Driver function.

In some embodiments, a Multiple Convolve function (“mconv”) isimplemented. This mconv function convolves the input signal (i.e., “x”)with an impulse response “h” multiple times, where the number of timesis set by the value “n”. This function implements a multiple cascade ofkernels, where kernel “h” would in this example be the impulse responseof a basic derivative kernel Kd_(p) 301 or the impulse response of abasic integrator kernel Ki_(p) 401 with the order of the kernel set by“Nd” or “Ni”, respectively, depending upon the respective call fromdifilter, and is reflected in the following code:

mconv(x,h,n) // Parameters/Arguments // x = filter output or input value// h = order of derivative kernel // n = scaled derivative value y = x;for i = 1:n {  y = conv(y,h); }

The output signal “y” resulting from the multiple convolution isreturned, in this example, to difilter.

The System_Driver function, in some embodiments, calls a Time-FrequencyAnalysis function (“tfAn”). This function performs a wavelet transformof input signal “data” using array of wavelets “cH”, by convolving eachwavelet with input signal “data”. Convolution is performed in thisexample by calling the Linear-Phase convolution function for eachwavelet, returning for each wavelet the resulting analytic componentsignal. In this example the convolution result is truncated to thecentral samples of the same length as the input “data”, such that azero-phase FIR filter is effectively realized with the wavelet. Thefunction tfAn takes the convolution results and constructs output matrix“X”. This matrix can be described as an N×M matrix, where N is along thetime dimension and M is along the components dimension, such that eachcolumn of a matrix “X” is an analytic component signal (i.e., a bandoutput). Matrix “X” is then returned to the System_Driver function.

In addition to the above-described functions and supporting pseudo-codeused to generate a filter bank, functionality is implemented to allowfor constructing quarter-phase objects or structures from components. Asa threshold matter, in some embodiments, a Get State function(“getState”) is called by a System_Driver function. This getStatefunction takes an analytic component signal matrix “X” as a parameter orargument, in this example, as would be returned by the function tfAn. Apseudo-code implementation of getState is as follows:

getState(X) // Parameters/arguments // X = an N × M matrix Nb =size(X,2); // number of components (bands) [sObj,sObjStrm] = getObj(X);// get object stream Nobj = length(sObjStrm); // number of objects instream // initialize state stream matrix and object index array S =zeros(Nobj,Nb); cS = cell(Nobj,1); // first recorded state @ firstobject (initial state is implicitly zero) iObj = 1; iState = 1; //update corresponding component dimension of state stream matrix with newqp S(iState,sObjStrm(iObj).Lcm) = sObjStrm(iObj).Lqp; // set qp label tozero on inactive component if ~sObjStrm(iObj).act { S(iState,sObjStrm(iObj).Lcm) = 0; } cS{iState} = iObj; // record objectindex (indices) @ this state iState = 2; iObj = 2; while iObj <= Nobj { // object-triggered state change  // check for “simultaneous” objects(objects appearing @ same sample)  if sObjStrm(iObj).iqp ~=sObjStrm(iObj−1).iqp  {   // object not simultaneous: copy previousstate forward   S(iState,:) = S(iState−1,:);  }  else  {   // objectsimultaneous with previous: stay at previous state   iState = iState−1; }  // update corresponding component dimension of state stream matrix // with new qp label  S(iState,sObjStrm(iObj).Lcm) =sObjStrm(iObj).Lqp;  // set qp label to zero on inactive component  if~sObjStrm(iObj).act  {   S(iState,sObjStrm(iObj).Lcm) = 0  }  // recordobject index (or indices) @ this state  cS{iState} = [cS{iState},iObj]; // next object, next state, iterate  iObj = iObj + 1;  iState =iState + 1; } // truncate excess zero/empty states (if any) due to“simultaneous” objects S = S(1:iState−1,:); // truncate excesszero/empty states (if any) cS = cS(1:iState−1);

GetState returns the following outputs: “S”, “cS”, “sObjStrm”, and“sObj”. Output “S” is a matrix representing a stream of state arraysconstructed from an object stream obtained from a matrix “X”. Eachcolumn of “S” corresponds to a component (i.e., the corresponding columnof “X”). Each row of “S” is a state array, here comprised ofquarter-phase labels (see discussion of “getObj” below) which are set tozero if the associated component is inactive. A new state array isformed upon the change of active quarter-phase of a component (i.e.,when a new object is formed). If more that one object is formedsimultaneously, then the state array reflects the change ofquarter-phase labels across all affected components. Output “cS” is anarray equal to the length of “S” (i.e., the total number of states).Each element of “cS” is an array containing the indices of theindividual data structures in the “sObj Strm” array of data structuresthat result in a corresponding state. As will be discussed below, output“sObj” is an array of structs, with each struct containing a pluralityof arrays. Arrays of structures are commonly understood in the art. (SeeC++ Primer Plus 3^(rd) Edition, by Stephen Prata, Sams Publishing,1998.) The output “sObjStrm” is an array of structs.

The states obtained via the getState function are defined by objects. Insome embodiments, the objects are an array of structs, indexed simply byobject index. In some embodiments, the objects are embedded into anarray of structs, the array indexed according to the originatingcomponent dimension, with each struct of the array of structs containinga plurality of arrays indexed by object index. In some embodiments, aGetObjects function (“getObj”) is described that accepts a matrix “X” asdescribed above, and returns the array of structs, indexed simply byobject index, in the above-described “sObjStrm”, and also returns thearray of structs, indexed according to originating component dimension,with each struct of the array of structs containing a plurality ofarrays indexed by object index, in the above described “sObj”.Pseudo-code representing this getObj function is outlined below:

getObj(X) // Parameters/arguments // X = an N × M matrix // number ofcomponents (bands) Nb = size(X,2); // define and build object structuresObj(Nb) = struct(‘iqp’,[ ],‘Lqp’,[ ],‘amp’,[ ],‘act’,[ ]); for i=1:Nb { // get qp representation (stream) for component  [siqp,iqps,Lqps] =findQP(X(:,i));  // build objects from component into struct forassociated component  sObj(i).iqp = iqps; // (time) index array ofobject occurrences (down  a column of X)  sObj(i).Lqp = Lqps; //quarter-phase label array per function findQP  sObj(i).amp =abs(X(iqps,i)); // object amplitude array  activeThr =0.0625*max(sObj(i).amp); // establish “activity threshold”  // activestatus of component flag array for each object  sObj(i).act =(sObj(i).amp > activeThr); } // initialize an array of structs sObjStrm= struct(‘iqp’,[ ],‘Lqp’,[ ],‘amp’,[ ],‘act’,[ ],‘Lcm’,[ ]); // buildindex-ordered object stream iObj = [ ]; // index (time) of object //append object info. from each component into single (sortable) array c =1; // object counter for i=1:Nb {  iObj = [iObj ; sObj(i).iqp]; // keeptrack of object indices  for j=1:length(sObj(i).iqp)  {  sObjStrm(c).iqp = sObj(i).iqp(j); // assign (time) index  sObjStrm(c).Lqp = sObj(i).Lqp(j); // assign qp label   sObjStrm(c).amp= sObj(i).amp(j); // assign amplitude   sObjStrm(c).act =sObj(i).act(j); // assign comp activity   sObjStrm(c).Lcm = i; // assigncomp associated w/object   c = c + 1; // next object, iterate }} // sortobjects into ordered stream by order of appearance (index in time)[iObjs,iiObjs] = sort(iObj); sObjStrm = sObjStrm(iiObjs);

As described above, the getObj function returns both “sObjStrm” and“sObj”. These return values are then returned to System_Driver by thegetState function where they are used in the display of data, in someembodiments, graphically, or used for the purpose of analysis ofcomponent signals, original input signals or the like.

Object indices and quarter-phase labels for specific structs or objectsare derived from a Find QP function (“findQP”). This function takes ananalytic component signal “x” and from this component signal extractsarrays of quarter-phase values labeled as: 1, 2, 3, and 4 (correspondingto the above-described labels A, B, C, and D). The getObj functionretrieves this information for each analytic component by calling findQPpassing in for “x” each corresponding column of “X”. The code for thisfindqp function is as follows:

findQP(x) // x = analytic component signal // initialize an array ofquarter-phase index structs siqp = struct(‘rp’,[ ],‘ip’,[ ],‘rn’,[],‘in’,[ ]); // indices for transitions into quarter-phases, with findzc// finding the indices of positive zero crossings siqp.rp =findzc(real(x)); // Label A (rp) positive-going zero crossings ofreal(x) siqp.ip = findzc(imag(x)); // Label B (ip) positive-going zerocrossings of imag(x) siqp.rn = findzc(−real(x)); // Label C (rn)negative-going zero crossings of real(x) siqp.in = findzc(−imag(x)); //Label D (in) negative-going zero crossings of imag(x) // buildindex-ordered quarter-phase stream // quarter-phase labels Lrp =ones(length(siqp.rp),1)*1; // labels A or 1 Lip =ones(length(siqp.ip),1)*2; // labels B or 2 Lrn =ones(length(siqp.rn),1)*3; // labels C or 3 Lin =ones(length(siqp.in),1)*4; // labels D or 4 // append qp indices andlabels into single (sortable) arrays iqp =[siqp.rp;siqp.rn;siqp.ip;siqp.in]; Lqp = [Lrp;Lrn;Lip;Lin]; // sort qpindices and labels by order of appearance [iqps,iiqps] = sort(iqp); Lqps= Lqp(iiqps);

There are three outputs for findQP: “siqp”, “iqps”, and “Lqps”. Output“siqp” is a structure of arrays containing quarter-phase point indices,members arranged by label (i.e., A, B, C, D), each member an array ofindices down a column of “X” at which occur qp points of the typecorresponding to that label. Output “iqps” is an array of quarter-phasetransition indices, for all label types, sorted in order of increasingvalue (i.e., order of appearance), and output “Lqps” is an array oflabels for quarter-phases being entered at corresponding quarter-phasetransition points indicated by array “iqps”.

The above-described functions (i.e., System_Driver, GetState, GetObject)use structs and arrays of structs whose fields contain a plurality ofarrays to store and manipulate data such as, for example, components,states and the like. As has been described elsewhere these structs,common to many structured programming regimes such as Matlab™ or the Cprogramming language, can be replaced with objects common to anobject-oriented programming paradigm such as C++, C#™, Java™ or Delphi™.Additionally, as is discussed above, these structs and/or objects can beorganized into not only arrays, but list, stack, queue, tree, heap, hashtable or some other structure known in the art. (See Algorithms inC++3^(rd) Edition, by Robert Sedgewick, Addison-Wesley, 1998.)Additionally, any portion of these functions that utilize iterativemethods to traverse a data structure, could, in the alternative, use arecursive method. Again, a determination of exact manner in which thesestructs and/or objects would be organized would beimplementation-specific, and hence left up to the particular softwaredeveloper's efficiency, and speed requirements.

FIG. 10A, FIG. 10B, FIG. 10C, and FIG. 10D depict various ways ofreflecting the plotting of various component representations of an inputECG signal read in from a file using the above-described calling treeand functions represented in the above pseudo-code.

FIG. 10A is a graph 1000 depicting a plot of the input ECG signal andthe reconstruction sums of the real parts and the imaginary parts of thecomponents resulting from the parallel-form analytic filter-bankdecomposition of the input ECG signal.

FIG. 10B is a graph 1001 of the five analytic components resulting fromthe decomposition, one for each band used in the example, with thecenter frequency of each respective band marked on the left of eachsubplot. On each plot, the real part of the analytic component signalappears in the solid traces, and the imaginary part of the analyticcomponent signal appears in the dashed traces. The quarter-phasetransition points for each component are marked on the real-part traces,using a “dot” (“.”) where the component is active, and an “x” where thecomponent is considered inactive (not active).

FIG. 10C is graph 1002 of the real-part component sum reconstruction ofthe input ECG signal, with all corresponding quarter-phase transitionpoints marked where the associated component is active.

Finally, FIG. 10D is a graph 1003 of a specific segment taken from FIG.10C in the range of 260 msec to 420 msec, with quarter-phase transitionpoints marked and corresponding state labels denoted. The state labelsare arranged each in a vertical stack of quarter-phase labels, with thequarter-phase label from lowest-frequency component on top andincreasing in frequency toward the bottom.

Any of the above-described methods, filter banks, or data structuresmaybe described and implemented using a computer programming language.In some embodiments, the Parallel-Form Kovtun-Ricci Wavelet Transform,and/or Cascade-Form Kovtun-Ricci Wavelet Transform described above canbe implemented using the C++ programming language in conjunction withcertain programming language libraries such as Microsoft FoundationClass (“MFC”). C++ and MFC are well known in the art of computer scienceand software engineering. (See Introduction to MFC Programming withVisual C++, by Richard M. Jones, Prentice Hall PTR, 1999.)

In some embodiments, as described below, ECG data from a file serves asinput for a program written in C++ and MFC (“the Program”). This Program(a copy of which is filed with U.S. Provisional Patent Application No.60/656,630 referenced above) utilizes a cascade-form filter bank todivide up the base wave signal or original signal into its componentparts. Once divided, each component wave is broken up intoquarter-phases, and the data reflecting each quarter-phase is storedinto an object, with each object horizontally and vertically linked toother objects. These objects are organized in a linked list structure, astructure well known in the art. Once linked, the data from theseobjects is retrieved and outputted into a wave representation.

The below matrix depicts the formation of a set of seven composite bandsformed from certain sums of band outputs y_(p) taken over contiguousranges of p. In the Program, a bank of N=250 filter sections isimplemented. Within each filter section, the component filter H_(p) iscomprised of only the scaled derivative kernel Hd_(p) (i.e., the scaledintegrator kernel Hi_(p) is not used, or equivalently, it is set so thatw_(p)=1). The Hd_(p) are tuned so that N_(d)=16 and k_(p)=p for p=1 . .. N.

Band Designation Range of p “Noise” 1-5 “40 Hz” 6-8 “20 Hz”  9-14 “10Hz” 16-28  “5 Hz” 36-49 “2.5 Hz”  82-99 “1.25 Hz”   191-249

The component bands for this example implementation are thus the sevencomposite bands formed from the respective sums of filter sections. Theband designations for these component bands are based upon theapproximate center frequency of the composite bands resulting from thecorresponding ranges of p and assuming a sample rate of 500 Hz. One maynote that this Program example does not use all filter section outputsy_(p), and, in theory, leaves small holes in the continuous spectrum.This is not problematic provided the component band outputs or filtersection outputs, or composite band outputs substantially cover thesupport of the power spectrum of the original signal. An alternateembodiment would be that the sum of the component outputs represents anacceptable approximation of the original signal.

FIG. 11 shows a graphical illustration of the output data for theProgram. In this Figure, a normal sinus rhythm (NSR) 1101 (i.e., the toptrace) taken from ECG vector V2 is depicted. The traces below the toptrace (i.e., Nos. 1102, 1103, 1104, 1105, 1106, 1107) show the componentsignals resulting from this original signal corresponding to the outputsof the component bands designated 40 Hz, 20 Hz, 10 Hz, 5 Hz, 2.5 Hz, and1.25 Hz, respectively.

FIG. 12 describes the decomposition of another NSR ECG 1210, along witha set of resulting component signals, in this case, those from componentbands designated 20, 10, 5, and 2.5 Hz respectively, from the topmostcomponent trace (i.e., Nos. 1201, 1202, 1203, and 1204). In this figure,thin-broken vertical lines are drawn at the beginning of each first,second and third quarter-phase transition of each cycle for the 5-Hzcomponent 1203 (for clarity of viewing, the vertical line is omitted atthe beginning of each fourth quarter-phase transition), from which maybe observed features on the original signal corresponding to thequarter-phases for this component. A bottom trace 1205 shows thereconstruction resulting from these four components. The thin-brokenvertical lines are drawn extending upward through the original signal1210 and downward through the reconstructed signal 1205, in order toshow the corresponding times of the beginning of each first, second andthird quarter-phase transition of each cycle of the 5-Hz componentsignal 1203.

FIG. 13 and FIG. 14 illustrate example base quasi-periodic waveforms andcorresponding components, and certain resulting quarter-phases andstates of interest, that reveal subtle morphological changes from normalECG morphology due to an abnormal condition, in this case acutemyocardial infarction (MI) in a human patient. As before, the traces arearranged such that the ECG is the top trace, and each successive tracebelow is the component signal arising from a component band designated40 Hz, 20 Hz, 10 Hz, 5 Hz, 2.5 Hz, or 1.25 Hz, respectively. FIG. 13corresponds to the MI case and FIG. 14 corresponds to the normal case.Note, particularly in FIG. 14, the region 1401 in the 5-Hz component,which corresponds to the small upward deflection 1402 in the normal ECGtrace 1403, and the substantial absence in the MI ECG 1301 of both aregion corresponding to this region 1401 and the deflection 1402.

FIG. 15A shows segmentation of the original signal ECG by state. Eachbox represents one member of the set of 16 possible states correspondingto the quarter-phase objects from the 1.25- and 2.5-Hz components (i.e.,component waves). Many of the boxes shown here, such as referencenumbers 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, and 1510,each contain a plurality of trace segments. Each trace segmentrepresents a piece of the original quasi-periodic waveform associatedwith a respective one of the states. Within each box, correspondingtrace segments arising from each of a plurality of depolarizations ofthe heart are overlaid one on top of another, resulting in a compositetrace. Inside some of the boxes (i.e., reference numbers 1501-1510),vertical ECG deflection is depicted and indexed bottom to top, andforward time is indexed left to right (i.e., each box represents apiece, or slice, of the original signal). While a particular state isactive, the original signal is traced in value and time within thecorresponding box. The trace starts at the left of the corresponding box(e.g., 1501), at the time that the corresponding state becomes activeand ends when the state transitions to another state. When thatparticular state is entered again (e.g., during the following cardiaccycle), the trace is overlaid on top of the previous trace. Thus, goodsegmentation is indicated by consistent traces over many similar cardiaccycles, indicating that the states truly capture a unique localmorphological characteristic in the original signal.

FIG. 15B and FIG. 15C illustrate a consistent segmentation atprogressively higher state resolutions of 64 and 256 respectively. Thishigher resolution is achieved by including higher-frequency componentobjects and their corresponding states.

FIG. 16 shows an example of applying ad hoc pattern-detection methodsfor detection of the fundamental cardiac cycle and the onset and offsetof the QRS complex. Here, markers (i.e., Nos. 1601, 1062, 1603, 1604)were placed on the transition to particular states corresponding to thefundamental cardiac cycle, with the added qualification of an objectamplitude criterion (i.e., a component active criterion) to reducespurious generation of markers.

With regard to the structure of the Program itself, the Program containsthe following modules and classes in addition to those supplied withMFC: A Readcse.cpp is implemented that reads in ECG from a file. ALeadband class is utilized that performs ECG signal band decompositionfor each of the six (6) frequency bands (i.e., designated 1.25, 2.5, 5,10, 20 and 40 Hz; see FIG. 10A.). Next, a Qatband class is employed toperform ECG band analysis of each of these six (6) frequency bands.Finally a Readband.cpp and Readqat.cpp files are utilized to display thedecomposition and analysis results. (E.g., see Attachment C ofProvisional Patent Application 60/656,630 referenced above.)

As to the specifics of each of these modules and classes, Readcse.cpptypically reads in binary data from a file. The Leadband class thendecomposes this binary data (i.e., a base waveform) into variouscomponent waveforms using the cascade-form filter described in FIG. 5.Then, the data relating to these component waves (e.g., a 805, T4 802, l803) is used to fill the various data structures described in FIG. 8A,FIG. 8B and FIG. 8C. These data structures are implemented in theqatband class. Lastly, these data structures are displayed graphicallyvia either the Readband.cpp or Readqat.cpp files.

In the present example of the Program, the input file of the ECG data(“D_(—)00004.DCD”) contains 10 seconds of the ECG records for a 12-leadsstandard ECG. Only one lead V2 has been used in the example. Two outputfiles are generated by the program: d0004_(—)7.wbd and d0004_(—)7.qat.D0004_(—)7.wbd displays the results of the ECG decomposition into sixfrequency bands from 1.25 to 40 Hz, while d0004_(—)7.gat displays theresults of the ECG quarter-phase analysis (i.e., states).

FIG. 17 displays the decomposition of ECG lead V2 into six (6) frequencybands (See FIG. 10A.).

FIG. 18 illustrates the reconstruction of the various components and theECG data from the states and objects.

FIG. 19 depicts the output of the program for the three lowest frequencycomponents (i.e., Nos. 1701, 1702, 1703 of FIG. 17) in the form of atransition matrix of 64 states.

A Hardware Implementation of the Above Method Using a MultiplierlessArchitecture

In some embodiments, the above-described filter banks (i.e., paralleland cascading filter banks) can be implemented using a multiplierlessarchitecture. A multiplerless architecture is well known in the art andtypically uses a series of adders, such as ripple-carry adders (RCA) orcarry-save adders (CSA), to perform multiplication operations. (SeeDesign of Multiplerless, High-Performance, Wavelet Filter Banks withImage Compression Applications, by K. Kotteri, A. Bell, and J. E.Carletta, IEEE Transactions on Circuits and Systems, Vol. 51 No. 3,March 2004; Multiplierless Approximation of Transform with AdderConstraints, by Ying-Jui Chen, IEEE Signal Processing Letters, Vol. 9No. 11, November 2002.) The advantage of such an architecture is that itis relatively small and as such can be implemented in small devicesneeding relatively smaller power supplies. In some embodiments, such anarchitecture would use embedded circuits to implement the functionalityof the filter banks, whereas in other embodiments some type of firmwareimplementation would be utilized to represent the functionality of thefilter banks

Such a multiplierless architecture could be designed using VHSICHardware Description Language (VHDL). By using VHDL the logic of boththe filter and the multiplierless architecture used to create a hardwareimplementation of a filter could be determined. This logic could then beused to implement an embedded hardware example of the VHDL code. The useof VHDL to design and implement an embedded hardware configurationincluding filter banks is well known in the art. (See VHDL: ProgrammingBy Example 4^(th) Edition, by Douglas L. Perry, McGraw-HillProfessional, 2002.)

System Description Utilizing the Above Method, Data Structure, orProgram

As a threshold matter, the above-described method, data structure orprogram can be used in conjunction with any qp waveform. In someembodiments, this is a qp wave in the form of ECG data, while in otherembodiments it might be another type of qp waveform. With regard to qpwaveforms derived from ECG data, any system that obtains ECG data from,for example, a set of electrodes could employ the above-outlined method,data structure or program.

FIG. 20 is a block diagram of a system 2000 wherein data related to qpwaveforms (“Data”) is stored in a data base 2001. This Data serves asinput for a computer program utilizing the above-described method, datastructure and/or program contained on a computer-readable medium 2002,and executed by a central processing unit (CPU) 2003. In someembodiments, this CPU is an x86 series CPU, while in other embodimentsit is a CPU known in the art. Input to execute the program can, in someembodiments, come by way of a keyboard 2004. In still other embodiments,input to execute the program can come by way of a peripheral device suchas a mouse, light pen, touch screen, touch pad or any other input deviceknown in the art. In some embodiments, the described method can becalled by another program to execute and process wave data. Onceprocessed, data processed using the above-described method, datastructure and/or program can be outputted to a display 2005, or sent viaan internet 2006 to another user terminal 2007. In some embodiments, theoutput can be placed into a database 2001 or another database locatedoff-site.

FIG. 21 is a block diagram of a system 2100 wherein the original wavesignal is an ECG. In this system, a set of electrodes 2101 (“leads”) isdisposed around, on, or within a human body in a manner to produce adesired differential voltage across them representing the voltagegradient generated by the electrical properties of the cardiac tissue.One or more electrodes 2101 may be disposed on the skin surface of thebody, underneath the skin surface (i.e., a subcutaneous application),and/or additionally, within the heart (i.e., an intracardiacapplication). Two electrical poles 2102 (“poles”) are formed by tyingtogether two disjoint sets, each of one or more such disposed electrodes2101. The term “vector” is used in this context interchangeably for thedirected voltage gradient and/or the simple voltage difference acrossthe poles. The term “leads” is used interchangeably with the polesand/or the leads from which they are constructed, but generally impliesa construction of one or more poles from the set of individual leads.

The leads 2101 are connected to a sensing device 2103 (“Device”) whichis either implanted into the human body or resides external to the humanbody. The Device 2103 in this example contains a set of units ormodules, including a difference-amplifier module, an analog-to-digitalconverter 2104 (“ADC”), a microprocessor unit 2105 (“MPU”), associatedcomputer-readable medium (“CRM”) 2106 containing program instructions,memory storage 2107 (“Storage”), and a power source for supplying power2108 (“Power Supply”) to the contained modules. The Device may alsocontain a communications module 2109 (“Comm”) and an associatedcommunications link 2110. This communication module and link, in someembodiments, may have a wireless means of providing ECG data.

The differential voltage existing across the leads is measured and/orsensed by presenting the leads to a difference amplifier 2102. In someembodiments, the difference amplifier is presumed to contain analogfiltering sufficient to substantially meet predetermined requirementsfor signal-to-noise ratio (“SNR”) optimization and/or anti-aliasing. Inthis example, the output of the difference amplifier is digitized usingan ADC 2104. The output samples from the ADC 2104 may then be used asthe original signal for this invention example. This original signal ispassed to the MPU 2105 for processing.

Multiple sensing vectors may be accommodated with multiple leadarrangements, as is well understood and established in the art. (See AnIntroduction to Electrocardiography 7^(th) Edition, by Leo Schamroth,Blackwell Scientific, 1990.) In this case, each differential lead paircould connect to its own processing chain and can be defined here toinclude a difference amplifier followed by an ADC 2104, producing acorresponding original signal for each sensing vector so processed. Eachresulting original signal would then be passed to the MPU 2105 forprocessing.

The MPU 2105 executes software and/or firmware stored oncomputer-readable medium 2106 to perform the various tasks necessary forbasic function of the Device, and/or to generally fulfill feature andfunctionality requirements of the Device. The storage of data valuesrepresenting the original signal and/or the associated objects can, insome embodiments, be in the storage 2107 or the computer-readable medium2106.

In some embodiments, the communications module 2109 is used to sendoutgoing transmissions from the Device 2103 to a Host 2201 for furtherprocessing, evaluation, storage and/or transmission to other similar ordifferent Hosts on a network. The communications module 2109 is alsoused to receive incoming transmissions from a Host 2201, which may bethe same as or distinct from the Host 2201 used for outgoingtransmissions. In some embodiments, incoming transmissions may includecommands, data, and/or firmware/software updates as necessary to performthe basic function of the Device 2103, and/or to generally fulfillfeature requirements of the Device 2103. For an implanted Device 2103,the connection between the communications module 2109 and a Host 2201would be typically implemented in wireless fashion, using methodsincluding but not limited to magnetic, capacitive, and/or RF, all wellunderstood and established in the art. (See Principles of WirelessNetworks: A Unified Approach 1^(st) Edition, by Kaveh Pahlavan, PrashantKrishnamurthy, Prentice Hall, 2001.) For an external Device, theconnection may be wireless or wired using methods also well understoodand established in the art.

FIG. 22 is a block diagram of a system 2200 that depicts a Host 2201which may, in some embodiments, include a set of units or modules suchas a communications module 2202 (“Comm”) and associated communicationslink 2209 for communicating with the Device 2103, a microprocessor unit2203 (MPU) including a computer-readable medium 2204 containing a set ofapplication instructions, an associated storage module 2205 (“Storage”)containing data values representing the original signal, aHuman-Computer Interface 2206 (“HCI”) by which an operator (e.g., aphysician, technician, or patient) may interact with the MPU 2203 andwhich may include a visual display, auditory display, keyboard, mouse,touch screen, haptic device, or other means of interaction. Alsodescribed is an Input/Output module 2207 (“I/O”) for generallyconnecting to, and communicating with, computer peripherals such as forexample printers, scanners, and/or data acquisition or imagingequipment, and a network communications module 2208 (“Net”) forcommunicating with other Hosts on a computer network such as Ethernetand/or a wireless network, or WiFi. A Power Supply unit is not drawn forthe Host, as such a unit may exist entirely within any or all of theindividual modules, and so is assumed to exist in the Host system asneeded.

FIG. 23 is a block diagram of a system 2300. In some embodiments, theInput/Analysis Process block 2301 takes the original signal and producesa corresponding stream of objects. The original signal is applied to theSignal Decomposition block 2302, the output of which is the set ofcorresponding component signals. These signals are then processed in theFractional Phase Representation block 2303 to identify the objectboundaries and measure basic attributes, and the correspondingobject-related information is passed to the Object Construction andLinking block 2304 to construct the object streams, filling out whateveradditional object-related, data-structure-related information (i.e.,attributes, links, etc.) is needed in a particular application. In casesof multiple original signals, the Input/Analysis Process block would berepeated and/or duplicated for each original signal. The resultingobject streams from each Process may then be merged into a singlecomposite stream of objects.

The Interpretive Process block 2305 takes the object stream and producesan interpretation of the original signal(s). In some embodiments, theState Construction block 2306 takes the object stream and constructsstates from it. The resulting series of states, along with theunderlying objects by which they are defined, then form the input to theOrganized Mapping block 2307, where the information is mapped in statespace and/or a vector space along one or more object attributes. Asstated in the context of this invention, the information may be mappeddirectly in some ad hoc manner, for example using a sequence detector onthe states and/or some nonlinear, neural and/or fuzzy map formed on theobject attributes. In some embodiments, the information may also be usedfor training a model such as an HMM and/or NN. Once trained, theinformation may be applied to the model to produce a mapped output. Insome embodiments, the mapped information is then passed to a PatternRecognition, Discrimination and/or Display block 2308 to transform themapped information into a human-interpretable form, such as for examplean automated identification and/or diagnosis of a certain condition,and/or visualization of relevant mapped information. Automatedidentification could involve simple thresholding on the mappedinformation, or could use more sophisticated detection anddiscrimination techniques such as Novelty Detection and Support VectorMachines. (See The Nature of Statistical Learning Theory 2^(th) Edition,by V. Vapnik, Springer 1995; Support-Vector Learning, by C. Cortes andV. Vapnik, 20 Machine Learning 1995.)

The Storage/Transmission block 2309 takes the object stream and/or theoriginal signal samples and/or samples of the component signals (or apredetermined select subset of the component signals) and stores some orall of them in memory and/or transmits some or all of them over acommunications link. The Resynthesis/Output Process block 2310 takes theobject stream from a communications link and/or storage and reconstructsan estimate of the original signal(s). The object stream correspondingto a desired original signal is recovered from storage and/or receivedfrom a communications link via the Retrieval/Reception block 2311.Estimates of the component signals for the desired original signal arethen produced by the Component Reconstruction block 2312, and thecomponent signal estimates are then combined in the SignalReconstruction block 2313 to produce an estimate of the desired originalsignal. If desired, the individual component signals may be output fromthe Resynthesis/Output Process block 2310 as well. Multiple originalsignals may be reconstructed using multiple instances of this Process,once for each desired original signal. The reconstructed signal(s) maythen be displayed, for example, on a plot trace (or series of plottraces) for human interpretation, if desired, along with the output ofthe Interpretive Process block 2305.

In some embodiments, device 2103 performs no signal-analysis processesexcept for the process of Storage/Transmission. The original signalsamples are merely passed to the communications module 2109 of theDevice, either directly or perhaps via the MPU 2105, perhaps with someformatting of the data for optimal transmission. The Host 2201 thenperforms some or all of the remainder of the signal-analysis processes.

In some embodiments, device 2103 performs the Input analysis and theStorage/Transmission Processes, and the Host 2201 performs some or allof the remainder of the processes. In the case that the componentsignals and/or the original signal are not transmitted to the Host 2201,they may be estimated on the Host end from the transmitted objects viathe Resynthesis/Output Process block 2310.

In some embodiments, the signal-analysis processes are performed, inwhole or in part, by execution of software or firmware on the MPU 2105and/or on the MPU 2203, using a program stored in the appropriaterespective computer-readable medium 2204 and/or storage unit 2205. Suchprocessing may also be performed, in whole or in part, in dedicatedhardware which in this example resides entirely within the MPU 2105and/or on the MPU 2203, for example in the form of microcode, in theform of a coprocessor, and/or in the form of peripheral hardware.

In some embodiments, the invention provides a method that includes:receiving a series of digital values representing a quasi-periodicwaveform, decomposing the series of digital values into a plurality ofcomponent signals, wherein each component signal includes a series ofdigital values that are obtained using a cascade of additive linearcombinations of substantially equal-magnitude versions of at least twoof the digital values of the quasi-periodic waveform, and storing thecomponent signals.

In some embodiments, the linear combinations use coefficients with equalmagnitudes but opposite sign, applied to each of two of the digitalvalues.

In some embodiments, the linear combinations use coefficients with equalmagnitudes and signs applied to each of the two or more of the digitalvalues of the quasi-periodic waveform.

In some embodiments, the coefficients are applied to sequential digitalvalues separated from one another by a scaling factor.

In some embodiments, the sequential digital values enter into the linearcombination, the number of values determined by a scaling factor.

In some embodiments, the series of values for each component signalrepresents a digitally filtered frequency-limited band of thequasi-periodic waveform.

In some embodiments, the digitally filtered frequency-limited band isselected based on frequency content of the quasi-periodic waveform.

In some embodiments, the series of values for each component signalinclude complex numbers and represent an analytic signal.

In some embodiments, the quasi-periodic waveform includes a time-varyingphysiological signal obtained from a mammal, and the series of digitalvalues for each component signal represents a digitally filteredfrequency-limited band of the quasi-periodic waveform.

In some embodiments, the quasi-periodic waveform includes anelectrocardiogram (ECG) signal from a human, and the series of digitalvalues for each component signal represents a digitally filteredfrequency-limited band of the ECG signal.

Some embodiments further include transforming each of the componentsignals to obtain: a plurality of independent ordinate values, and aplurality of abscissa values each associated with a correspondingordinate value. Each ordinate value is based on an amplitude metric.Each abscissa value is based on a time metric.

In some embodiments, transforming of each component signal furtherincludes: a phase adjustment value such that a linear combination of thecomponent signals substantially reconstructs the quasi-periodicwaveform.

Some embodiments further include transforming of each component signalfurther includes: an amplitude adjustment value such that a linearcombination of the component signals substantially reconstructs thequasi-periodic waveform.

Some embodiments further include transforming of each component signaluses a multiplierless architecture.

In some embodiments, the component signals are obtained by applying aParallel-Form Kovtun-Ricci Wavelet Transform to the series of digitalvalues.

In some embodiments, the component signals are obtained by applying aCascade-Form Kovtun-Ricci Wavelet Transform to the series of digitalvalues.

In some embodiments of the invention, a computer-readable medium isemployed having executable instructions stored thereon for causing asuitable programmed central processing unit to perform a method thatincludes: obtaining a series of digital values representing aquasi-periodic waveform, decomposing the series of digital values into aplurality of component signals, wherein each component signal includes aseries of digital values that are obtained using a cascade of additivelinear combinations of substantially equal-magnitude versions of atleast two of the digital values, and storing the component signals.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the linear combination to use coefficientswith equal magnitudes but opposite sign, applied to each of two of thedigital values.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the linear combination to use coefficientswith equal magnitude and sign applied to each of the two or more of thedigital values.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the coefficients to be applied to sequentialdigital values separated from one another by a scaling factor.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the sequential digital values to be enteredinto the linear combination, with the number of values determined by ascaling factor.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the series of values for each componentsignal to represent a digitally filtered frequency-limited band of thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the digitally filtered frequency-limitedband to be chosen corresponding to a frequency content of thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the series of values for each componentsignal to represent an analytic signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform to be atime-varying physiological signal obtained from a mammal, and the seriesof digital values for each component signal represent a digitallyfiltered frequency-limited band of the quasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform to be anelectrocardiogram (ECG) signal from a human, and the series of digitalvalues for each component signal represent a digitally filteredfrequency-limited band of the ECG signal.

In some embodiments, the computer-readable medium includes furtherinstructions for transforming each of the component signals to obtain: aplurality of independent ordinate values, and a plurality of abscissavalues each associated with a corresponding ordinate value.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to be based on anamplitude metric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each abscissa value to be based on a timemetric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the transforming of each component signalincluding: a phase adjustment value such that a linear combination ofthe component signals substantially reconstructs the quasi-periodicwaveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the transforming of each component signalincluding: an amplitude adjustment value such that a linear combinationof the component signals substantially reconstructs the quasi-periodicwaveform.

In some embodiments, the computer-readable medium includes furtherinstructions for transforming of each component signals using amultiplierless architecture.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the component signals to be obtained byapplying a Parallel-Form Kovtun-Ricci Wavelet Transform to the series ofdigital values.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the component signals to be obtained byapplying a Cascade-Form Kovtun-Ricci Wavelet Transform to the series ofdigital values.

In some embodiments, the invention provides a method that includesobtaining a plurality of component signals representative of aquasi-periodic waveform, wherein each component signal includes aplurality of component values that represent a digitally filteredfrequency-limited band of the quasi-periodic waveform, and transformingeach of the plurality of component values into a fractional-phaserepresentation including values representing at least one of an abscissaand an ordinate for each of a plurality of fractional phases per localcycle of the component signal, and storing the fractional-phaserepresentation. In some embodiments, each ordinate value is based on anamplitude metric. In some embodiments, each abscissa value is based on atime metric.

In some embodiments, each fractional-phase representation includes aphase adjustment value such that a linear combination of the pluralityof component values substantially reconstructs the quasi-periodicwaveform.

In some embodiments, each fractional-phase representation includes anamplitude normalization value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the component signal includes a first fractionalphase, a second fractional phase, a third fractional phase, and a fourthfractional phase, wherein: the fractional-phase representation for thefirst fractional phase includes a value representing an abscissaassociated with a zero crossing of the associated component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate and a value representing an abscissaboth values representing an abscissa and values representing an ordinateassociated with a maximum of the associated component signal, thefractional-phase representation for the third fractional phase includesa value representing an abscissa associated with another zero crossingof the associated component signal, and the fractional-phaserepresentation for the fourth fractional phase includes a valuerepresenting an ordinate and a value representing an abscissa bothassociated with a minimum of the associated component signal. Eachordinate value is based on an amplitude metric. Each abscissa value isbased on a time metric. Each fractional-phase representation includes aphase adjustment value such that a linear combination of the pluralityof component values substantially reconstructs the quasi-periodicwaveform. Each fractional-phase representation includes an amplitudenormalization value such that a linear combination of the plurality ofcomponent values substantially reconstructs the quasi-periodic waveform.

In some embodiments, the plurality of component values for eachcomponent signal are complex-valued, wherein the fractional-phaserepresentation for each fractional phase includes a value representingan ordinate, a value representing an abscissa, or both valuesrepresenting an abscissa and values representing an ordinate associatedwith an angular range of a complex argument of an associated analyticcomponent signal.

In some embodiments, each ordinate value includes an amplitude metricselected from a group consisting of: a complex magnitude, a real part,and an imaginary part of the analytic component signal.

In some embodiments, each ordinate value includes an amplitude metricbased upon: the complex magnitude of the analytic component signal, thereal part of the analytic component signal, and the imaginary part ofthe analytic component signal.

In some embodiments, each abscissa value is based on a time metric.

In some embodiments, each fractional phase representation includes aphase adjustment value such that a linear combination of the pluralityof component values substantially reconstructs the quasi-periodicwaveform.

In some embodiments, each fractional-phase representation includes anamplitude normalization value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the number of fractional phases available per localcycle of each component signal is a predetermined number for thatcomponent, and wherein the angular ranges for the phases correspond tosubdivisions of the angular range of the complex argument.

In some embodiments, each ordinate value is based upon an amplitudemetric selected from a group consisting of: a complex magnitude of theanalytic component signal, a real part of the analytic component signal,and an imaginary part of the analytic component signal.

In some embodiments, each ordinate value is based upon an amplitudemetric including: a complex magnitude of the analytic component signal,a real part of the analytic component signal, and an imaginary part ofthe analytic component signal. In some embodiments, each abscissa valueis based upon a time metric.

In some embodiments, each fractional-phase representation includes aphase adjustment value such that a linear combination of the pluralityof component values substantially reconstructs the quasi-periodicwaveform.

In some embodiments, each fractional-phase representation includes anamplitude normalization value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the number of fractional phases available per localcycle of the component signal is four including: a first fractionalphase, a second fractional phase, a third fractional phase, and a fourthfractional phase, and wherein: the fractional-phase representation forthe first fractional phase includes a value representing an ordinate, avalue representing an abscissa, or both values representing an abscissaand values representing an ordinate associated with a zero crossing of areal part of the associated analytic component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate, a value representing an abscissa, orboth values representing an abscissa and values representing an ordinateassociated with a zero crossing of an imaginary part of the associatedanalytic component signal, the fractional-phase representation for thethird fractional phase includes a value representing an ordinate, avalue representing an abscissa, or both values representing an abscissaand values representing an ordinate associated with another zerocrossing of the real part of the associated analytic component signal,and the fractional-phase representation for the fourth fractional phaseincludes a value representing an ordinate, a value representing anabscissa, or both values representing an abscissa and valuesrepresenting an ordinate associated with another zero crossing of theimaginary part of the associated analytic component signal.

In some embodiments, each ordinate value includes an amplitude metricselected from a group consisting of: a complex magnitude of the analyticcomponent signal, a real part of the analytic component signal, and animaginary part of the analytic component signal.

In some embodiments, each ordinate value includes an amplitude metricconsisting of: the complex magnitude of the analytic component signal,the real part of the analytic component signal, and the imaginary partof the analytic component signal.

In some embodiments, each abscissa value is based upon a time metric.

In some embodiments, each fractional-phase representation includes aphase adjustment value such that a linear combination of the pluralityof component values substantially reconstructs the quasi-periodicwaveform.

In some embodiments, each fractional-phase representation includes anamplitude normalization value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the component signal is an electrocardiogram (ECG)signal from a human.

In some embodiments, the zero crossing is evaluated by finding a changein sign.

In some embodiments, the abscissa for each zero crossing is evaluated byperforming an interpolation in a neighborhood of the sign change.

In some embodiments, the interpolation is a curve fit and the zerocrossing is evaluated by solving for a root.

In some embodiments, the amplitude metric is obtained from aquarter-phase.

In some embodiments, the invention provides a computer-readable mediumhaving executable instructions stored thereon for causing a suitableprogrammed central processing unit to perform a method that includes:obtaining a plurality of component signals representative of aquasi-periodic waveform, wherein each component signal includes aplurality of component values that represent a digitally filteredfrequency-limited band of the quasi-periodic waveform, and transformingeach of the plurality of component values into a fractional-phaserepresentation including values representing an abscissa, an ordinate,or both values representing an abscissa and values representing anordinate for each of a plurality of fractional phases per local cycle ofthe component signal, and storing the fractional-phase representation ina storage device. In some embodiments, the computer-readable mediumincludes further instructions configured for each ordinate value to bebased on an amplitude metric. In some embodiments, the computer-readablemedia allowing each abscissa value to be based on a time metric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude a phase adjustment value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for a fractional-phase representation to includean amplitude normalization value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments of the invention, the computer-readable mediumincludes further instructions configured for the component signal toinclude a first fractional phase, a second fractional phase, a thirdfractional phase, and a fourth fractional phase, and includes furtherinstructions configured such that: the fractional-phase representationfor the first fractional phase includes a value representing an abscissaassociated with a zero crossing of the associated component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate and a value representing an abscissaboth values representing an abscissa and values representing an ordinateassociated with a maximum of the associated component signal, thefractional-phase representation for the third fractional phase includesa value representing an abscissa associated with another zero crossingof the associated component signal, and the fractional-phaserepresentation for the fourth fractional phase includes a valuerepresenting an ordinate and a value representing an abscissa bothassociated with a minimum of the associated component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to be based on anamplitude metric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each abscissa value to be based on a timemetric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude a phase adjustment value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude an amplitude normalization value such that a linear combinationof the plurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for component signals that are analytic suchthat the plurality of component values for each component signal arecomplex-valued, and wherein the fractional-phase representation for eachfractional phase includes a value representing an ordinate, a valuerepresenting an abscissa, or both values representing an abscissa andvalues representing an ordinate associated with an angular range of acomplex argument of an associated analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to comprise an amplitudemetric selected from a group consisting of: a complex magnitude of theanalytic component signal, a real part of the analytic component signal,and an imaginary part of the analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to comprise an amplitudemetric based upon: the complex magnitude of the analytic componentsignal, the real part of the analytic component signal, and theimaginary part of the analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each abscissa value to be based on a timemetric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude a phase adjustment value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude an amplitude normalization value such that a linear combinationof the plurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the number of fractional phases availableper local cycle of each component signal to be a predetermined numberfor that component, and wherein the angular ranges for the fractionalphases correspond to subdivisions of the angular range of the complexargument.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to be based upon anamplitude metric selected from a group consisting of: a complexmagnitude of the analytic component signal, a real part of the analyticcomponent signal, and an imaginary part of the analytic componentsignal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for ordinate value to be based upon an amplitudemetric including: a complex magnitude of the analytic component signal,a real part of the analytic component signal, and an imaginary part ofthe analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each abscissa value to be based upon a timemetric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude a phase adjustment value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude an amplitude normalization value such that a linear combinationof the plurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the component signal to include a firstfractional phase, a second fractional phase, a third fractional phase,and a fourth fractional phase, and also includes further instructionsconfigured for: the fractional-phase representation for the firstfractional phase includes a value representing an ordinate, a valuerepresenting an abscissa, or both values representing an abscissa andvalues representing an ordinate associated with a zero crossing of areal part of the associated analytic component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate, a value representing an abscissa, orboth values representing an abscissa and values representing an ordinateassociated with a zero crossing of an imaginary part of the associatedanalytic component signal, the fractional-phase representation for thethird fractional phase includes a value representing an ordinate, avalue representing an abscissa, or both values representing an abscissaand values representing an ordinate associated with another zerocrossing of the real part of the associated analytic component signal,and the fractional-phase representation for the fourth fractional phaseincludes a value representing an ordinate, a value representing anabscissa, or both values representing an abscissa and valuesrepresenting an ordinate associated with another zero crossing of theimaginary part of the associated analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to include an amplitudemetric selected from a group consisting of: a complex magnitude of theanalytic component signal, a real part of the analytic component signal,and an imaginary part of the analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value to include an amplitudemetric consisting of: the complex magnitude of the analytic componentsignal, the real part of the analytic component signal, and theimaginary part of the analytic component signal,

In some embodiments, the computer-readable medium includes furtherinstructions configured for each abscissa value to be based upon a timemetric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude a phase adjustment value such that a linear combination of theplurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each fractional-phase representation toinclude an amplitude normalization value such that a linear combinationof the plurality of component values substantially reconstructs thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform to be anelectrocardiogram (ECG) signal from a human.

In some embodiments, the computer-readable media is configured for thezero crossing to be evaluated by finding a change in sign.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the zero crossing to be evaluated by findinga change in sign.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the zero crossing to be evaluated byperforming interpolation in the neighborhood of the sign change.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the interpolation to be a curve fit and thezero crossing is evaluated by solving for a root.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the amplitude metric to be obtained from aquarter-phase.

In some embodiments, the invention provides a computer-readable mediumhaving executable instructions stored thereon for causing a suitableprogrammed information processor to perform a method that includes:obtaining a plurality of fractional-phase representations of a signal,wherein each fractional-phase representation includes valuesrepresenting an abscissa, an ordinate, or both values representing anabscissa and values representing an ordinate for each of a plurality offractional phases of a component of a plurality of components that eachrepresent a digitally filtered frequency-limited band of aquasi-periodic waveform, and assigning to each fractional-phaserepresentation a phase label associated with an active fractional phaseof the fractional-phase representation, generating a plurality of datastructures, each data structure arising from each component associatedwith a different abscissa value and including a phase label, and storingthe plurality of data structures in a storage device.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each ordinate value is based upon anamplitude metric.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the associated component to be an analyticsignal and each ordinate value is derived from an amplitude metricselected from a group consisting of: a complex magnitude of the analyticcomponent signal, a real part of the analytic component signal, and animaginary part of the analytic component signal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the associated component to be an analyticsignal and each ordinate value is derived from an amplitude metric basedupon: a complex magnitude of the analytic component signal, a real partof the analytic component signal, and an imaginary part of the analyticcomponent signal.

In some embodiments, the computer-readable media allows the ordinate tobe based upon a statistical measure.

In some embodiments, the computer-readable medium includes furtherinstructions configured for each of the abscissa values to be based upona time metric.

Some embodiments of the invention include a computer-readable mediahaving stored thereon a first data structure that includes: a descriptorthat includes an abscissa value from an associated fractional-phaserepresentation, an optional descriptor that includes an ordinate valuefrom the associated fractional-phase representation, an optionaldescriptor that includes an abscissa interval value representing aninterval of the abscissa over which an associated fractional phase isactive, an optional descriptor that includes a label identifying aparticular data structure from the plurality of data structures, anoptional descriptor that includes a label identifying an associatedcomponent from the plurality of components, an optional descriptor thatincludes a label identifying the quasi-periodic waveform, an optionaldescriptor that includes a value representing a local period of theassociated component, an optional descriptor that includes a valuerepresenting an interval from a reference abscissa value to anassociated abscissa value, an optional descriptor that includes a valuerepresenting a relative measure between the ordinate value associatedwith the fractional phase, and an ordinate value associated with thecomponent, the quasi-periodic waveform, or both the component and thequasi-periodic waveform, and an optional descriptor that includes alabel indicating that the component is active for the active fractionalphase associated with the particular data structure, based uponcomparing the ordinate value to a predetermined threshold.

In some embodiments of the computer-readable medium having the datastructure stored thereon, each ordinate value is based upon an amplitudemetric.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the associated component is an analytic signaland each ordinate value is derived from an amplitude metric selectedfrom a group consisting of: a complex magnitude of the analyticcomponent signal, a real part of the analytic component signal, and animaginary part of the analytic component signal.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the associated component is an analytic signaland each ordinate value is derived from an amplitude metric based upon:a complex magnitude of the analytic component signal, a real part of theanalytic component signal, and an imaginary part of the analyticcomponent signal.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the abscissa values are based upon a timemetric.

Some embodiments of the computer-readable medium having the datastructure stored thereon include an atomic period value, wherein theatomic period value is the abscissa interval value multiplied by anumber of fractional phases available in the fractional-phaserepresentation.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the data structure further includes links toother data structures, including: a descriptor that includes a link toanother particular data structure associated with the component, whereinthe associated abscissa value of the other data structure is less thanthe abscissa value associated in the particular data structure, anoptional descriptor that includes a link to another particular datastructure associated with the component, wherein the abscissa value ofthe other data structure is greater than the abscissa value in theparticular data structure, an optional descriptor that includes a linkto another particular data structure associated with the component,wherein the abscissa value of the other data structure is less than theabscissa value in the particular data structure, and wherein the phaselabel of the other data structure is of the same value as the phaselabel of the particular data structure, and an optional descriptor thatincludes a link to another particular data structure associated with thecomponent, wherein the abscissa value of the other data structure isgreater than the abscissa value in the particular data structure, andwherein the phase label of the other data structure is of the same valueas the phase label of the particular data structure.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the data structure further includes: adescriptor that includes a difference of abscissa values between theabscissa value included in the particular data structure and theabscissa value included in a first linked data structure relative tothis particular data structure, an optional descriptor that includes adifference of abscissa values between the abscissa value included in theparticular data structure and the abscissa value included in a secondlinked data structure relative to this particular data structure, anoptional descriptor that includes an indication of deviation from anexpected sequence of phase labels, and an optional descriptor thatincludes a moving average of abscissa values for a group of datastructures surrounding the particular data structure.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the data structure is organized within aplurality of other data structures into an array based upon a commonassociated component.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the data structure further includes a label tothe data structure, wherein a label describes a transition of thecomponent to and from an active condition.

In some embodiments of the computer-readable medium having the datastructure stored thereon, the data structure further includes a firstphase label associated with a first fractional phase of a givencomponent, a second phase label associated with a second fractionalphase of the given component, a third phase label associated with athird fractional phase of the given component, and a fourth phase labelassociated with a fourth fractional phase of the given component.

Some embodiments of the invention include a computer-readable mediumhaving the data structure stored thereon, wherein the data structurefurther includes: a descriptor including an abscissa interval valuerepresenting the interval of the abscissa over which the associatedfractional phase is active to the first data structure, and a pluralityof optional descriptors including at least three selected from the setcomprising: an optional descriptor that identifies the first datastructure from the plurality of data structures, an optional descriptoridentifying the associated component from the plurality of components,an optional descriptor assigning a label identifying the quasi-periodicwaveform, an optional descriptor assigning a value to representing thelocal period of the associated component, an optional descriptorassigning a referenced abscissa value representing the interval from areference abscissa value to the associated abscissa value, an optionaldescriptor assigning a link to another data structure associated withthe component, wherein the included abscissa value of the other datastructure is less than the abscissa value, an optional descriptorassigning a link to another data structure associated with thecomponent, wherein the included abscissa value of the other datastructure is greater than the abscissa value included in the first datastructure, an optional descriptor assigning to the first data structurea link to a second data structure associated with the component, whereinthe second data structure is an upper neighbor, an optional descriptorassigning to the first data structure a link to a third data structureassociated with the component, wherein the third data structure is alower neighbor, an optional descriptor assigning to the first datastructure a relative T value, an optional descriptor assigning to thefirst data structure a left harmonic value, an optional descriptorassigning to the first data structure a right harmonic value, anoptional descriptor assigning to the first data structure an amplitudemeasurement, an optional descriptor assigning to the first datastructure a phase label, an optional descriptor assigning to the firstdata structure a left object, an optional descriptor assigning to thefirst data structure a right object, and an optional descriptorassigning to the first data structure a relative amplitude value.

Some embodiments of the computer-readable medium further include aplurality of data structures organized as a list based upon a commonassociated component.

Some embodiments of the computer-readable medium further include aplurality of data structures organized as a tree based upon a commonassociated component.

Some embodiments of the computer-readable medium further include aplurality of data structures organized as a hash table based upon acommon associated component.

Some embodiments of the computer-readable medium further include aplurality of data structures organized as a heap based upon a commonassociated component.

Some embodiments of the computer-readable medium further include aplurality of data structures organized as an array based upon a commonassociated component.

Some embodiments of the invention provide a method that includes:obtaining one or more data structures, organizing the one or more datastructures into an arranged data structure, wherein the one or morearranged data structures are arranged according to their associatedcomponent, and the fractional phases associated with the one or moredata structures within each arranged data structure are co-active over arange of abscissa values for an associated quasi-periodic waveform,generating a plurality of states, each state including a group of one ormore arranged data structures, and storing the states in a storagedevice.

In some embodiments, the plurality of states include a state with areference abscissa value associated with the abscissa values of thearranged data structure, and assigned to the state, and the plurality ofstates are arranged into a state sequence, according to the associatedabscissa values.

In some embodiments, the reference abscissa value is the greatest of theabscissa values of the arranged data structure assigned to thecorresponding state.

In some embodiments of the method, a new state is formed upon a changeof an active fractional phase of one or more of the arranged datastructures in the state, and forming a sequence of states.

In some embodiments, the method further includes: analyzing a statetrajectory in state space, and identifying a set of features within aquasi-periodic waveform, a component signal, or both the quasi-periodicwaveform and the component signals based upon the state space.

In some embodiments of the method, the quasi-periodic waveform signal isan electrocardiogram (ECG) signal from a mammal, with featuresincluding: a P wave, a Q wave, an R wave, an S wave, a T wave, a U wave,and any ECG segment delineated by any pair of these waves.

In some embodiments of the method, the quasi-periodic waveform signal isan electrocardiogram (ECG) signal from a mammal, with features selectedfrom a group consisting of: a P wave, a Q wave, an R wave, an S wave, aT wave, a U wave, and any ECG segment delineated by any pair of thesewaves.

In some embodiments of the method, the quasi-periodic waveform signal isa heart-sound signal from a mammal, and the features include: an S1, anS2, and an S3 sound.

In some embodiments of the method, the quasi-periodic waveform signal isa heart-sound signal from a mammal and includes features selected from agroup consisting of: an S1, an S2, and an S3 sound.

In some embodiments, the quasi-periodic waveform signal is ablood-pressure signal from a mammal, and the features include a dicroticnotch.

In some embodiments, the quasi-periodic waveform signal is a blood-flowsignal from a mammal. In some embodiments, the quasi-periodic waveformsignal is an electroencephalogram (EEG) signal from a mammal. In someembodiments, the quasi-periodic waveform signal is an electromyogram(EMG) signal from a mammal. In some embodiments, the quasi-periodicwaveform signal is an electrooculogram (EOG) signal from a mammal. Insome embodiments, the quasi-periodic waveform signal is a respiratorysignal from a mammal, wherein the respiratory signal an optionaldescriptor includes: pressure, flow, and volume information.

Some embodiments of the method further include: analyzing a statetrajectory in state space, and identifying a condition subset from apredetermined set of conditions associated with a quasi-periodicwaveform, a component signal, or both the quasi-periodic waveform andthe component signals based upon the state space analysis.

In some embodiments, the quasi-periodic waveform signal is anelectrocardiogram (ECG) signal from a mammal. In some embodiments, thequasi-periodic waveform signal is a heart-sound signal from a mammal. Insome embodiments, the quasi-periodic waveform signal is a blood-pressuresignal from a mammal. In some embodiments, the quasi-periodic waveformsignal is a blood-flow signal from a mammal. In some embodiments, thequasi-periodic waveform signal is an EEG, EMG, or EOG signal from amammal. In some embodiments, the quasi-periodic waveform signal is arespiratory signal from a mammal, wherein the respiratory signal anoptional descriptor includes pressure, flow, and volume information. Insome embodiments, the quasi-periodic waveform signal is derived from amammal and a condition set include an optional descriptor the conditionnormal, and an optional descriptor the condition abnormal.

In some embodiments of the method, the condition set includes cardiacconditions, the normal condition is normal sinus rhythm, and theabnormal condition is a set of rhythm abnormalities including: anoptional descriptor bradycardia and tachycardia abnormality, and anoptional descriptor conduction abnormality, wherein bradycardia is anoptional descriptor set including sick sinus syndrome, and tachycardiais an optional descriptor set including atrial flutter, atrialfibrillation, ventricular tachycardia, and ventricular fibrillation, andconduction abnormality is an optional descriptor set to include bundlebranch block, AV and node block.

In some embodiments, the method wherein: the condition set includescardiac conditions, the normal condition is normal sinus rhythm, and theabnormal condition is a set of structural abnormalities including:optional ischemia, optional myocardial infarction, optional leftventricular hypertrophy, optional right ventricular hypertrophy,optional bi-ventricular hypertrophy, optional long QT syndrome, andoptional tall T syndrome.

In some embodiments, the set conditions are normal blood pressure andabnormal blood pressure. In some embodiments, the method wherein the setconditions include hypotension and hypertension.

Some embodiments of the invention provide a method that includes:obtaining a state sequence resulting from each of one or morequasi-periodic waveforms comprising a training set, wherein eachquasi-periodic waveform corresponds to a predetermined set of featuretypes and assigned by feature type in a known manner to a set of statesforming a state subsequence of the associated state sequence,constructing a sequence detector for each state subsequence resultingfrom a training set, such that detection of the state subsequence is anindication of the assigned feature type, and identifying the featuretype associated with each feature of an arbitrary test quasi-periodicwaveform, based upon a pattern-matching operation.

In some embodiments, the pattern-matching operation includes:establishing a degree of match measure between the sequences associatedwith each detector and the state sequence resulting from the testwaveform, and forming a fuzzy inference upon these measures through afuzzy logic mapping.

In some embodiments, the pattern-matching operation includes:establishing a degree of match measure between the state sequenceassociated with each detector and the state sequence resulting from thetest waveform, and applying a neural network mapping to these measures,wherein the neural network is trained on the training set.

In some embodiments, the method further includes: obtaining the statesequence resulting from each of one or more quasi-periodic waveformscomprising a training set, each waveform assigned in a known manner to asubset of a set of one or more predetermined conditions, constructing asequence detector for each state sequence resulting from the trainingset, such that detection of the state sequence is an indication of theassigned condition subset, and identifying the condition subsetassociated with an arbitrary test quasi-periodic waveform, based upon apattern-matching operation.

In some embodiments, the pattern-matching operation includes:establishing a degree of match measure between the sequences associatedwith each detector and the state sequence resulting from the testwaveform, and forming a fuzzy inference upon these measures through afuzzy logic mapping.

In some embodiments, the pattern-matching operation includes:establishing a degree of match measure between the sequence associatedwith each detector and the state sequence resulting from the testwaveform, and applying a neural network mapping to these measures,wherein the neural network is trained on the training set.

In some embodiments, the method further comprising: obtaining a statesequence resulting from each of one or more quasi-periodic waveformscomprising a training set, each feature of a set of features in eachwaveform corresponding to a predetermined set of feature types andassigned by feature type in a known manner to a set of states forming asubsequence of the associated state sequence, constructing a modelaccording to a span of a state vector or a subspace of that span instate space, training the model based upon the state subsequencesresulting from a training data set, including associated feature sets aslabels for supervised learning, and identifying a feature typeassociated with each feature of an arbitrary test quasi-periodicwaveform, based upon statistics in the trained model in relation to thestate subsequences resulting from test waveform features.

In some embodiments, the model has multiple modalities. In someembodiments, the model includes a plurality of submodels. In someembodiments, the submodel is a Hidden Markov Model, a Neural Network, ora Fuzzy Logic Mapping. In some embodiments, the model is a Hidden MarkovModel, a Neural Network, or a Fuzzy Logic Mapping.

Some embodiments further include obtaining a state sequence resultingfrom each of one or more quasi-periodic waveforms comprising a trainingset, each waveform assigned in a known manner to a subset of a set ofone or more predetermined conditions, constructing a model according toa span of a state vector or a subspace of that span in state space,training the model based upon the state sequences resulting from atraining data set, including associated condition sets as labels forsupervised learning, and identifying the condition subset associatedwith an arbitrary test quasi-periodic waveform, based upon statistics inthe trained model in relation to the state sequence resulting from atest waveform.

In some embodiments, the model has multiple modalities. In someembodiments, the model includes a plurality of submodels. In someembodiments, the submodel is a Hidden Markov Model, a Neural Network, ora Fuzzy Logic Mapping. In some embodiments, the model is a Hidden MarkovModel, a Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the invention includes obtaining a state sequenceresulting from each of one or more quasi-periodic waveforms comprising atraining set, waveforms in aggregation containing an unknown set offeatures generally falling into groups, these features each having anassociated set of states forming a subsequence of the associated statesequence, constructing a model according to a span of a state vector ora subspace of that span in state space, training the model based uponthe state subsequences resulting from the training data set, allowingfor self-organization of the clusters in state-space, assigning labelsto the emergent clusters and associating them with corresponding featuretypes on the quasi-periodic waveform, and identifying the feature typeassociated with each feature of an arbitrary test quasi-periodicwaveform, based upon statistics in the trained model in relation to thestate subsequences resulting from test waveform features.

In some embodiments, the model has multiple modalities. In someembodiments, the model includes a plurality of submodels. In someembodiments, the submodel is a Hidden Markov Model, a Neural Network, ora Fuzzy Logic Mapping. In some embodiments, the model is a Hidden MarkovModel, a Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the invention includes obtaining the state sequenceresulting from each of one or more quasi-periodic waveforms comprising atraining set, the waveforms in aggregation pertaining to one or moreunknown conditions of a set of conditions of unknown size, thesewaveforms each having an associated state sequence, constructing a modelaccording to the span of the state vector or a subspace of that span instate space, training the model based upon the state sequences resultingfrom the training data set, allowing for self-organization of theclusters in state-space, and assigning labels to the emergent clustersand associating them with condition types, and identifying the set ofcondition types associated with an arbitrary test quasi-periodicwaveform, based upon the statistics in the trained model in relation tothe state sequence resulting from the test waveform. In someembodiments, the model has multiple modalities. In some embodiments, themodel includes a plurality of submodels. In some embodiments, thesubmodel is a Hidden Markov Model, a Neural Network, or a Fuzzy LogicMapping. In some embodiments, the model is a Hidden Markov Model, aNeural Network, or a Fuzzy Logic Mapping. In some embodiments, thestates are pruned from the state trajectory based upon the relative timeof habitation within the states. In some embodiments, the states arepruned from the state trajectory based upon the relative time ofhabitation within the states.

Some embodiments of the invention provide a computer-readable mediumhaving executable instructions stored thereon for causing a suitableprogrammed central processing unit to perform a method that includes:obtaining one or more data structures, organizing the one or more datastructures into an arranged data structure, wherein the one or morearranged data structures are arranged according to their associatedcomponent, and the fractional phases associated with the one or moredata structures within each arranged data structure are co-active over arange of abscissa values for an associated quasi-periodic waveform,generating a plurality of states, each state including a group of one ormore arranged data structures, and storing the states in a storagedevice.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the plurality of states to contain a statewith a reference abscissa value associated with the abscissa values ofthe arranged data structure, and assigned to the state, and theplurality of states are arranged into a state sequence, according to theassociated abscissa values.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the reference abscissa value to be thegreatest of the abscissa values of the arranged data structure assignedto the corresponding state.

In some embodiments, the computer-readable medium includes furtherinstructions configured for a new state to be formed upon a change of anactive fractional phase of one or more of the arranged data structuresin the state, and forming a sequence of states.

In some embodiments, the computer-readable medium includes furtherinstructions configured for analyzing a state trajectory in state space,and identifying a set of features within a quasi-periodic waveform, acomponent signals, or both the quasi-periodic waveform and the componentsignals based upon the state space.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be anelectrocardiogram (ECG) signal from a mammal, with features including: aP wave, a Q wave, an R wave, an S wave, a T wave, a U wave, and any ECGsegment delineated by any pair of these waves.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be anelectrocardiogram (ECG) signal from a mammal, with features selectedfrom a group consisting of: a P wave, a Q wave, an R wave, an S wave, aT wave, a U wave, and any ECG segment delineated by any pair of thesewaves.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be aheart sound signal from a mammal, and the features include: an S1, anS2, and an S3 sound.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be aheart sound signal from a mammal and includes features selected from agroup consisting of: an S1, an S2, and an S3 sound.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be ablood pressure signal from a mammal, and the features include a dicroticnotch.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be ablood flow signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be anEEG, an EMG, or a EOG signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be arespiratory signal from a mammal, further comprising allowing therespiratory signal an optional descriptor includes: pressure, flow, andvolume information.

In some embodiments, the computer-readable medium includes furtherinstructions configured for analyzing a state trajectory in state space,and identifying a condition subset from a predetermined set ofconditions associated with a quasi-periodic waveform, a componentsignal, or both the quasi-periodic waveform and the component signalsbased upon the state space analysis.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be anelectrocardiogram (ECG) signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be aheart-sound signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be ablood-pressure signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be ablood-flow signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be anEEG, EMG or an EOG signal from a mammal.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to be arespiratory signal from a mammal, further comprising allowing therespiratory signal an optional descriptor includes: pressure, flow, andvolume information.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the quasi-periodic waveform signal to bederived from a mammal and a condition set including: an optionaldescriptor the condition normal, and an optional descriptor thecondition abnormal.

In some embodiments, the computer-readable medium includes furtherinstructions configured such that the condition set includes cardiacconditions, the normal condition is normal sinus rhythm, and theabnormal condition is a set of rhythm abnormalities including anoptional descriptor bradycardia and tachycardia abnormality, and anoptional descriptor conduction abnormality, further comprising allowingbradycardia is an optional descriptor set including sick sinus syndrome,and tachycardia is an optional descriptor set including atrial flutter,atrial fibrillation, ventricular tachycardia, and ventricularfibrillation, and conduction abnormality is an optional descriptor setto include bundle branch block, AV and node block.

In some embodiments, the computer-readable medium includes furtherinstructions configured such that the condition set includes cardiacconditions, the normal condition is normal sinus rhythm, and theabnormal condition is a set of structural abnormalities including:optional ischemia, optional myocardial infarction, optional leftventricular hypertrophy, optional right ventricular hypertrophy,optional bi-ventricular hypertrophy, optional long QT syndrome, andoptional tall T syndrome.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the set conditions to be normal bloodpressure and abnormal blood pressure.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the set conditions to include hypotensionand hypertension.

In some embodiments, the computer-readable medium includes furtherinstructions configured for obtaining a state sequence resulting fromeach of one or more quasi-periodic waveforms comprising a training set,further comprising allowing each quasi-periodic waveform corresponds toa predetermined set of feature types and assigned by feature type in aknown manner to a set of states forming a state subsequence of theassociated state sequence, constructing a sequence detector for eachstate subsequence resulting from a training set, such that detection ofthe state subsequence is an indication of the assigned feature type, andidentifying the feature type associated with each feature of anarbitrary test quasi-periodic waveform, based upon a pattern-matchingoperation.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the pattern-matching operation includes:establishing a degree of match measure between the sequences associatedwith each detector and the state sequence resulting from the testwaveform, and forming a fuzzy inference upon these measures through afuzzy logic mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the pattern-matching operation includes:establishing a degree of match measure between the state sequencesassociated with each detector and the state sequence resulting from thetest waveform, and applying a neural network mapping to these measures,further comprising allowing the neural network is trained on thetraining set.

In some embodiments, the computer-readable medium includes furtherinstructions configured for obtaining the state sequence resulting fromeach of one or more quasi-periodic waveforms comprising a training set,each waveform assigned in a known manner to a subset of a set of one ormore predetermined conditions, constructing a sequence detector for eachstate sequence resulting from the training set, such that detection ofthe state sequence is an indication of the assigned condition subset,and identifying the condition subset associated with an arbitrary testquasi-periodic waveform, based upon a pattern-matching operation.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the pattern-matching operation includes:establishing a degree of match measure between the sequences associatedwith each detector and the state sequence resulting from the testwaveform, and forming a fuzzy inference upon these measures through afuzzy logic mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the pattern-matching operation includes:establishing a degree of match measure between the sequences associatedwith each detector and the state sequence resulting from the testwaveform, and applying a neural network mapping to these measures,further comprising allowing the neural network is trained on thetraining set.

In some embodiments, the computer-readable medium includes furtherinstructions configured for obtaining a state sequence resulting fromeach of one or more quasi-periodic waveforms comprising a training set,each feature of a set of features in each waveform corresponding to apredetermined set of feature types and assigned by feature type in aknown manner to a set of states forming a subsequence of the associatedstate sequence, constructing a model according to a span of a statevector or a subspace of that span in state space, training the modelbased upon the state subsequences resulting from a training data set,including associated feature sets as labels for supervised learning, andidentifying a feature type associated with each feature of an arbitrarytest quasi-periodic waveform, based upon statistics in the trained modelin relation to the state subsequences resulting from test waveformfeatures. In some embodiments, the computer-readable medium includesfurther instructions configured for the model to have multiplemodalities.

In some embodiments, the computer-readable medium includes furtherinstructions configured for producing the model to include a pluralityof submodels.

In some embodiments, the computer-readable medium includes furtherinstructions configured for producing the submodel as a Hidden MarkovModel, Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the model to be a Hidden Markov Model,Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for obtaining a state sequence resulting fromeach of one or more quasi-periodic waveforms comprising a training set,each waveform assigned in a known manner to a subset of a set of one ormore predetermined conditions, constructing a model according to a spanof a state vector or a subspace of that span in state space, trainingthe model based upon the state sequences resulting from a training dataset, including associated condition sets as labels for supervisedlearning, and identifying the condition subset associated with anarbitrary test quasi-periodic waveform, based upon statistics in thetrained model in relation to the state sequence resulting from a testwaveform. In some embodiments, the computer-readable medium includesfurther instructions configured for the model to have multiplemodalities.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the model to have a plurality of submodels.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the submodel to be a Hidden Markov Model,Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the model to be a Hidden Markov Model,Neural Network, or Fuzzy Logic Mapping.

Some embodiments of the invention provide a computer-readable mediumthat includes instructions configured to perform a method that includes:obtaining a state sequence resulting from each of one or morequasi-periodic waveforms comprising a training set, waveforms inaggregation containing an unknown set of features generally falling intogroups, these features each having an associated set of states forming asubsequence of the associated state sequence, constructing a modelaccording to a span of a state vector or a subspace of that span instate space, training the model based upon the state subsequencesresulting from the training data set, allowing for self-organization ofthe clusters in state-space, assigning labels to the emergent clustersand associating them with corresponding feature types on thequasi-periodic waveform, and identifying the feature type associatedwith each feature of an arbitrary test quasi-periodic waveform, basedupon statistics in the trained model in relation to the statesubsequences resulting from test waveform features. In some embodiments,the computer-readable medium includes further instructions configuredfor the model to have multiple modalities. In some embodiments, thecomputer-readable medium includes further instructions configured forthe model to include a plurality of submodels.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the submodel to be a Hidden Markov Model,Neural Network, or Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the model to be a Hidden Markov Model,Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for obtaining the state sequence resulting fromeach of one or more quasi-periodic waveforms comprising a training set,the waveforms in aggregation pertaining to one or more unknownconditions of a set of conditions of unknown size, these waveforms eachhaving an associated state sequence, constructing a model according tothe span of the state vector or a subspace of that span in state space,training the model based upon the state sequences resulting from thetraining data set, allowing for self-organization of the clusters instate-space, and assigning labels to the emergent clusters andassociating them with condition types, and identifying the set ofcondition types associated with an arbitrary test quasi-periodicwaveform, based upon the statistics in the trained model in relation tothe state sequence resulting from the test waveform. In someembodiments, the computer-readable medium includes further instructionsconfigured for the model to have multiple modalities. In someembodiments, the computer-readable medium includes further instructionsconfigured for the model to include a plurality of submodels.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the submodel to be a Hidden Markov Model, aNeural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the model to be a Hidden Markov Model,Neural Network, or a Fuzzy Logic Mapping.

In some embodiments, the computer-readable medium includes furtherinstructions configured for allowing the states to be pruned from thestate trajectory based upon the relative time of habitation within thestates.

In some embodiments, the computer-readable medium includes furtherinstructions configured for allowing the states to be pruned from thestate trajectory based upon the relative time of habitation within thestates.

Some embodiments of the invention provide a method that includes:obtaining a plurality of data structures, assigning the data structureattributes to a reconstruction formula to substantially reconstruct theassociated component signal over the associated abscissa interval wherethe associated fractional phase is active, and generating a plurality ofone or more reconstructed component signals.

In some embodiments, the method allows the plurality of reconstructedcomponent signals to be linearly combined to substantially reconstructthe quasi-periodic waveform.

In some embodiments, the method allows the subset of the one or morereconstructed component signals to be linearly combined to substantiallyreconstruct a desired portion of the quasi-periodic waveform.

In some embodiments, the method further includes: obtaining a pluralityof data structures, performing data compression on the data structures,and storing the compressed data structures in a storage device.

In some embodiments, the method provides for compression to be performedthrough selective removal of data structures containingredundant/unremarkable information.

In some embodiments, the method provides for compression to be performedthrough selective removal of redundant/unremarkable information in thedata structures.

In some embodiments, the method provides for compression to be performedthrough data compression operations performed upon the attributeinformation in the data structures.

Some embodiments of the invention provide a computer-readable mediumhaving executable instructions stored thereon for causing a suitableprogrammed processing unit to perform a method that includes: obtaininga plurality of data structures, assigning the data structure attributesto a reconstruction formula to substantially reconstruct the associatedcomponent signal over the associated abscissa interval where theassociated fractional phase is active, and generating a plurality of oneor more reconstructed component signals.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the plurality of reconstructed componentsignals to be linearly combined to substantially reconstruct thequasi-periodic waveform.

In some embodiments, the computer-readable medium includes furtherinstructions configured for a subset of one or more reconstructedcomponent signals to be linearly combined to substantially reconstruct adesired portion of the quasi-periodic waveform.

Some embodiments of the invention provide a computer-readable mediumthat includes instructions for causing a suitable programmed centralprocessing unit to perform a method that includes: obtaining a pluralityof data structures, performing data compression on the data structures,and storing the compressed data structures in a storage device.

In some embodiments, the computer-readable media is described whereinthe compression is performed through selective removal of datastructures containing redundant/unremarkable information.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the compression to be performed throughselective removal of redundant/unremarkable information in the datastructures.

In some embodiments, the computer-readable medium includes furtherinstructions configured for the compression to be performed through datacompression operations performed upon the attribute information in thedata structures.

As used herein, a fractional phase is one of a plurality of fractionalphases in a fractional phase representation (i.e., an interval withinone or more cycles of a component signal representative of a digitallyfiltered frequency-limited band obtained from a quasi-periodicwaveform).

As used herein, a quarter-phase is one of four fractional phases in afractional phase representation (i.e., one of four intervals within onecycle of a component signal representative of a digitally filteredfrequency-limited band obtained from a quasi-periodic waveform).

As used herein, a quasi-periodic waveform is a signal that may beconsidered as the linear combination of a series of locally periodiccomponent signals, each having different periods and/or shapes, withlocality exhibited on the order of a cycle to a few cycles. Examples ofquasi-periodic waveforms that can be processed and analyzed by thepresent invention include but are not limited to:

general biological signals from humans or animals such as ECG waveforms,intracardiac electrogram (EGM) waveforms, EEG waveforms, EMG waveforms,EOG waveforms, blood pressure waveforms, plethysmography waveforms,heart sounds, ultrasound signals, MRI signals, bio-impedance waveforms,electro-dermal testing and polygraph signals;

natural phenomena such as solar sunspot cycles, climate patterns,patterns of El NiZo, drought cycles, seismic activity (i.e., earthquakesin certain areas occur approximately every N years, and majorearthquakes can be preceded by relatively minor seismic activity thatexhibit quasi-periodic characteristics), ocean currents and tides, andoccurrence of tsunamis;

manufacturing process signals for feedback and control;

demographics and societal patterns such as population cycles, productmarket preferences, crowd noise, traffic patterns, traffic noise, andindustrial noise;

vehicular signals pertaining to feedback, control, and detection anddiagnosis of issues associated with operation and/or service of avehicle, such as engine and transmission noise, temperature, informationfrom any number of different types of sensors such as oxygen sensors,temperature sensors, pressure sensors, and flow sensors;

aircraft performance-related signals, such as from laminar-flow sensors,pressure sensors, windshear sensors and downdraft sensors;

financial time series, representing for example prices of stocks,equities, securities, commodities, bonds, mutual funds, and futures, andeconomic cycles including micro and macro-economic phenomena;

content-carrying signals, such as music, speech or video signals(including analysis and compression of such signals);

telemetry signals for applications such as radar (including, forexample, Synthetic Aperture Radar (SAR), Terrain Following Radar (TFR),and Forward-Looking Infrared (FLIR) radar) and sonar;

communications signals such as transmitted over an optical fiber, cableor remote radio link (including spread-spectrum transmissions), such asweak signals from satellites and spacecraft or animal-tracking devices,cordless and cellular telephones, and wireless computer networks such asthose using the IEEE802.11g standard;

analysis of data storage medium signals such as from a magnetic diskhead or a laser optical receiver for optical storage media; and

other signals exhibiting quasi-periodic behavior, including seismicsignals obtained for oil exploration.

Other embodiments involving various permutations and distributions ofprocesses or blocks within the Device and Host are specificallycontemplated to fall within the scope of this current invention. Someembodiments include combinations or subcombinations of a plurality ofitems listed individually or within one or more groups of other items.

Some embodiments of the invention provide a method that includes:obtaining a component signal representative of a digitally filteredfrequency-limited band from a quasi-periodic waveform, wherein thecomponent signal includes a plurality of component values, reducing theplurality of component values into a fractional-phase representation ofN fractional phases where N is greater than one, and wherein eachfractional phase is associated with a phase label and includes one ormore values representing an abscissa, an ordinate, or both an abscissaand an associated ordinate for each of one or more local cycles of thecomponent signal, and storing the fractional-phase representation in adata structure on a storage device.

In some embodiments of the method, the fractional-phase representationfor one local cycle of the component signal includes a first fractionalphase, a second fractional phase, a third fractional phase, and a fourthfractional phase, and wherein the reducing of the plurality of componentvalues into the fractional-phase representation further includes:obtaining, for the first fractional phase, a value representing anabscissa of a zero crossing of the component signal, obtaining, for thesecond fractional phase, a value representing an ordinate and a valuerepresenting an abscissa of a maximum of the component signal,obtaining, for the third fractional phase, a value representing anabscissa of another zero crossing of the component signal, andobtaining, for the fourth fractional phase, a value representing anordinate and a value representing an abscissa of a minimum of thecomponent signal.

In some embodiments, each ordinate value is obtained based on anamplitude metric and each abscissa value is based on a time metric, andwherein the component signal is an electrocardiogram (ECG) signal from ahuman.

In some embodiments, each zero crossing is determined by finding achange in sign and interpolating in the neighborhood of the sign changeusing a curve fit.

In some embodiments, the maximum and the minimum of the component signalare each obtained by finding a change in sign in an estimate of thelocal derivative and interpolating in the neighborhood of the signchange using a curve fit.

In some embodiments, the method further includes: calculating afractional phase adjustment value for the fractional-phaserepresentation such that a linear combination of values derived from thefractional-phase representation with values derived from one or moreother fractional-phase representations obtained from other digitallyfiltered frequency-limited bands substantially reconstructs thequasi-periodic waveform.

In some embodiments, the component signal is analytic such that theplurality of component values for the component signal are complexnumbers, and wherein the fractional-phase representation for eachfractional phase is associated with an angular range of a complexargument of the analytic component signal.

In some embodiments, the fractional-phase representation for one localcycle of the component signal includes a first fractional phase, asecond fractional phase, a third fractional phase, and a fourthfractional phase, and wherein the reducing of the plurality of componentvalues into the fractional-phase representation further includes:obtaining, for the first fractional phase, a complex value representingan ordinate and a real value representing an abscissa of a zero crossingof a real part of the component signal, obtaining, for the secondfractional phase, a complex value representing an ordinate and a realvalue representing an abscissa of a zero crossing of an imaginary partof the component signal, obtaining, for the third fractional phase, acomplex value representing an ordinate and a real value representing anabscissa of another zero crossing of the real part of the componentsignal, and obtaining, for the fourth fractional phase, a complex valuerepresenting an ordinate and a real value representing an abscissa ofanother zero crossing of an imaginary part of the component signal.

In some embodiments, each zero crossing is determined by finding achange in sign and interpolating in the neighborhood of the sign changeusing a curve fit.

In some embodiments, each complex ordinate value is based on anamplitude metric and each real abscissa value is based on a time metric,and wherein the component signal is an electrocardiogram (ECG) signalfrom a human.

In some embodiments, the invention provides a computer-readable mediumhaving instructions thereon for causing a suitably programmedinformation processor to execute a method comprising: obtaining acomponent signal representative a digitally filtered frequency-limitedband from a quasi-periodic waveform, wherein the component signalincludes a plurality of component values, reducing the plurality ofcomponent values into a fractional-phase representation of N fractionalphases where N is greater than one, and wherein each fractional phase isassociated with a phase label and includes one or more valuesrepresenting an abscissa, an ordinate, or both an abscissa and anassociated ordinate for each of one or more local cycles of thecomponent signal, and storing the fractional-phase representation in adata structure on a storage device.

In some embodiments of the medium, the fractional-phase representationfor one cycle of the component signal includes a first fractional phase,a second fractional phase, a third fractional phase, and a fourthfractional phase, and wherein: the fractional-phase representation forthe first fractional phase includes a value representing an abscissa ofa zero crossing of the component signal, the fractional-phaserepresentation for the second fractional phase includes a valuerepresenting an ordinate and a value representing an abscissa of amaximum of the component signal, the fractional-phase representation forthe third fractional phase includes a value representing an abscissa ofanother zero crossing of the component signal, and the fractional-phaserepresentation for the fourth fractional phase includes a valuerepresenting an ordinate and a value representing an abscissa of aminimum of the component signal.

In some embodiments, each ordinate value is based on an amplitude metricand each abscissa value is based on a time metric, and wherein thecomponent signal is an electrocardiogram (ECG) signal from a human.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change using acurve fit.

In some embodiments, the maximum and the minimum of the component signalare each evaluated by finding a change in sign in an estimate of thelocal derivative and interpolating in the neighborhood of the signchange using a curve fit.

In some embodiments, the fractional-phase representation includes afractional phase adjustment value such that a linear combination ofvalues derived from the fractional-phase representation with valuesderived from one or more other fractional-phase representations obtainedfrom other digitally filtered frequency-limited bands substantiallyreconstructs the quasi-periodic waveform.

In some embodiments, the component signal is analytic such that theplurality of component values for the component signal are complexnumbers, and wherein the fractional-phase representation for eachfractional phase is associated with an angular range of a complexargument of the analytic component signal.

Some embodiments of the invention provide an apparatus wherein thefractional-phase representation for one local cycle of the componentsignal includes a first fractional phase, a second fractional phase, athird fractional phase, and a fourth fractional phase, and wherein: thefractional-phase representation for the first fractional phase includesa complex value representing an ordinate and a real value representingan abscissa of a zero crossing of a real part of the component signal,the fractional-phase representation for the second fractional phaseincludes a complex value representing an ordinate and a real valuerepresenting an abscissa of a zero crossing of an imaginary part of thecomponent signal, the fractional-phase representation for the thirdfractional phase includes a complex value representing an ordinate and areal value representing an abscissa of another zero crossing of the realpart of the component signal, the fractional-phase representation forthe fourth fractional phase includes a complex value representing anordinate and a real value representing an abscissa of another zerocrossing of an imaginary part of the component signal.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change using acurve fit.

In some embodiments, each complex ordinate value is based on anamplitude metric and each real abscissa value is based on a time metric,and wherein the component signal is an electrocardiogram (ECG) signalfrom a human.

Some embodiments of the invention provide an apparatus that includes astorage device, a source for a component signal representative of adigitally filtered frequency-limited band from a quasi-periodicwaveform, wherein the component signal includes a plurality of componentvalues, and means for reducing the plurality of component values into afractional-phase representation of N fractional phases where N isgreater than one, and wherein each fractional phase is associated with aphase label and includes one or more values representing an abscissa, anordinate, or both an abscissa and an associated ordinate for each of oneor more local cycles of the component signal, and for storing thefractional-phase representation on the storage device.

In some embodiments of the apparatus, the fractional-phaserepresentation for one local cycle of the component signal includes afirst fractional phase, a second fractional phase, a third fractionalphase, and a fourth fractional phase, and wherein: the fractional-phaserepresentation for the first fractional phase includes a valuerepresenting an abscissa of a zero crossing of the component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate and a value representing an abscissa ofa maximum of the component signal, the fractional-phase representationfor the third fractional phase includes a value representing an abscissaof another zero crossing of the component signal, and thefractional-phase representation for the fourth fractional phase includesa value representing an ordinate and a value representing an abscissa ofa minimum of the component signal.

In some embodiments, each ordinate value is based on an amplitude metricand each abscissa value is based on a time metric, and wherein thecomponent signal is an electrocardiogram (ECG) signal from a human.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change, e.g.,in some embodiments, interpolating using a curve fit.

In some embodiments, the maximum and the minimum of the component signalare each evaluated by finding a change in sign in an estimate of thelocal derivative and interpolating in the neighborhood of the signchange using a curve fit.

In some embodiments, the fractional-phase representation includes afractional phase adjustment value such that a linear combination ofvalues derived from the fractional-phase representation with valuesderived from one or more other fractional-phase representations obtainedfrom other digitally filtered frequency-limited bands substantiallyreconstructs the quasi-periodic waveform.

In some embodiments, the component signal is analytic such that theplurality of component values for the component signal are complexnumbers, and wherein the fractional-phase representation for eachfractional phase is associated with an angular range of a complexargument of the analytic component signal.

In some embodiments, the fractional-phase representation for one localcycle of the component signal includes a first fractional phase, asecond fractional phase, a third fractional phase, and a fourthfractional phase, and wherein: the fractional-phase representation forthe first fractional phase includes a complex value representing anordinate and a real value representing an abscissa of a zero crossing ofa real part of the component signal, the fractional-phase representationfor the second fractional phase includes a complex value representing anordinate and a real value representing an abscissa of a zero crossing ofan imaginary part of the component signal, the fractional-phaserepresentation for the third fractional phase includes a complex valuerepresenting an ordinate and a real value representing an abscissa ofanother zero crossing of the real part of the component signal, thefractional-phase representation for the fourth fractional phase includesa complex value representing an ordinate and a real value representingan abscissa of another zero crossing of an imaginary part of thecomponent signal.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change using acurve fit.

In some embodiments, each complex ordinate value is based on anamplitude metric and each real abscissa value is based on a time metric,and wherein the component signal is an electrocardiogram (ECG) signalfrom a human.

Some embodiments of the invention provide an apparatus that includes: astorage device, a source for a component signal representative, adigitally filtered frequency-limited band from a quasi-periodicwaveform, wherein the component signal includes a plurality of componentvalues, and a fractional-phase reducer that operates on the plurality ofcomponent values and generates a fractional-phase representation of Nfractional phases where N is greater than one, and wherein eachfractional phase is associated with a phase label and includes one ormore values representing an abscissa, an ordinate, or both an abscissaand an associated ordinate for each of one or more local cycles of thecomponent signal, and wherein the reducer is operatively coupled tostore the fractional-phase representation on the storage device.

In some embodiments of the apparatus, the fractional-phaserepresentation for one local cycle of the component signal includes afirst fractional phase, a second fractional phase, a third fractionalphase, and a fourth fractional phase, and wherein: the fractional-phaserepresentation for the first fractional phase includes a valuerepresenting an abscissa of a zero crossing of the component signal, thefractional-phase representation for the second fractional phase includesa value representing an ordinate and a value representing an abscissa ofa maximum of the component signal, the fractional-phase representationfor the third fractional phase includes a value representing an abscissaof another zero crossing of the component signal, and thefractional-phase representation for the fourth fractional phase includesa value representing an ordinate and a value representing an abscissa ofa minimum of the component signal.

In some embodiments, each ordinate value is based on an amplitude metricand each abscissa value is based on a time metric, and wherein thecomponent signal is an electrocardiogram (ECG) signal from a human.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change using acurve fit.

In some embodiments, the maximum and the minimum of the component signalare each evaluated by finding a change in sign in an estimate of thelocal derivative and interpolating in the neighborhood of the signchange using a curve fit.

In some embodiments, the fractional-phase representation includes afractional phase adjustment value such that a linear combination ofvalues derived from the fractional-phase representation with valuesderived from one or more other fractional-phase representations obtainedfrom other digitally filtered frequency-limited bands substantiallyreconstructs the quasi-periodic waveform.

In some embodiments, the component signal is analytic such that theplurality of component values for the component signal are complexnumbers, and wherein the fractional-phase representation for eachfractional phase is associated with an angular range of a complexargument of the analytic component signal.

In some embodiments, the number of fractional phases is four and thefractional-phase representation for one local cycle of the componentsignal includes a first fractional phase, a second fractional phase, athird fractional phase, and a fourth fractional phase, and wherein: thefractional-phase representation for the first fractional phase includesa complex value representing an ordinate and a real value representingan abscissa of a zero crossing of a real part of the component signal,the fractional-phase representation for the second fractional phaseincludes a complex value representing an ordinate and a real valuerepresenting an abscissa of a zero crossing of an imaginary part of thecomponent signal, the fractional-phase representation for the thirdfractional phase includes a complex value representing an ordinate and areal value representing an abscissa of another zero crossing of the realpart of the component signal, the fractional-phase representation forthe fourth fractional phase includes a complex value representing anordinate and a real value representing an abscissa of another zerocrossing of an imaginary part of the component signal.

In some embodiments, each zero crossing is evaluated by finding a changein sign and interpolating in the neighborhood of the sign change using acurve fit.

In some embodiments, each complex ordinate value is based on anamplitude metric and each real abscissa value is based on a time metric,and wherein the component signal is an electrocardiogram (ECG) signalfrom a human.

Some embodiments of the invention provide a computer-readable mediumhaving stored thereon a first data structure for a fractional-phaserepresentation, the data structure including: a descriptor that includesan abscissa value from the fractional-phase representation, a descriptorthat includes an ordinate value from the associated fractional-phaserepresentation, and a descriptor that includes an abscissa intervalvalue representing an interval over which an associated fractional phaseis active.

In some embodiments of the computer-readable medium, the first datastructure further includes three or more descriptors selected from agroup consisting of: a descriptor that includes a label identifying aparticular data structure from within a plurality of data structures, adescriptor that includes a label identifying the component from aplurality of components, a descriptor that includes a label identifyingthe quasi-periodic waveform, a descriptor that includes a valuerepresenting a local period of the associated component, a descriptorthat includes a value representing an interval from a reference abscissavalue to an associated abscissa value, a descriptor that includes avalue representing a relative measure between the ordinate valueassociated with the fractional phase, and an ordinate value associatedwith the component, the quasi-periodic waveform, or both the componentand the quasi-periodic waveform, and a descriptor that includes a labelindicating that the component is active for the active fractional phaseassociated with the particular data structure, based upon comparing theordinate value to a predetermined threshold.

In some embodiments, the first data structure further includes links toother data structures, including: a link to another particular datastructure associated with the component, wherein the associated abscissavalue of the other data structure is less than the abscissa valueassociated in the particular data structure, a link to anotherparticular data structure associated with the component, wherein theabscissa value of the other data structure is greater than the abscissavalue in the particular data structure, a link to another particulardata structure associated with the component, wherein the abscissa valueof the other data structure is less than the abscissa value in theparticular data structure, and wherein the phase label of the other datastructure is of the same value as the phase label of the particular datastructure, and a link to another particular data structure associatedwith the component, wherein the abscissa value of the other datastructure is greater than the abscissa value in the particular datastructure, and wherein the phase label of the other data structure is ofthe same value as the phase label of the particular data structure.

In some embodiments, the first data structure further includes linkedattributes, including: a descriptor that includes a difference ofabscissa values between the abscissa value included in the particulardata structure and the abscissa value included in a first linked datastructure relative to this particular data structure, a descriptor thatincludes a difference of abscissa values between the abscissa valueincluded in the particular data structure and the abscissa valueincluded in a second linked data structure relative to this particulardata structure, a descriptor that includes an indication of deviationfrom an expected sequence of phase labels, and a descriptor thatincludes a moving average of abscissa values for a group of datastructures surrounding the particular data structure.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Although numerous characteristics andadvantages of various embodiments as described herein have been setforth in the foregoing description, together with details of thestructure and function of various embodiments, many other embodimentsand changes to details will be apparent to those of skill in the artupon reviewing the above description. The scope of the invention should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein,” respectively. Moreover, the terms “first,” “second,” and“third,” etc., are used merely as labels, and are not intended to imposenumerical requirements on their objects.

What is claimed is:
 1. An apparatus comprising: a computer having astorage device; means for obtaining a digitized signal having a seriesof digital values of a quasi-periodic waveform, wherein thequasi-periodic waveform includes a physiological signal; means forperforming a decomposition on the digitized signal to obtaindecomposition components of the digitized signal; means, using thecomputer, for analyzing the decomposition components through afractional-phase transformation and forming a plurality of associateddata structures, wherein each of the plurality of data structurescontains information associated with local attributes of the associateddecomposition component; means, using the computer, for reducing thesize of the information contained in the plurality of data structuresbased on the information contained in the plurality of data structures;means for storing the reduced-size information in the storage deviceaccessible by the computer; and means for automatically diagnosing,using the computer and based on the plurality of associated datastructures, a physiological condition associated with the quasi-periodicwaveform, and generating relevant information from the diagnosis.
 2. Theapparatus of claim 1, further comprising: means for displaying avisualization of the relevant information.
 3. A computer-implementedmethod comprising: obtaining a digitized signal having a series ofdigital values of a quasi-periodic waveform, wherein the quasi-periodicwaveform includes a physiological signal; performing a decomposition onthe digitized signal to obtain decomposition components of the digitizedsignal; analyzing, using an information processor, the decompositioncomponents through a fractional-phase transformation and forming aplurality of associated data structures, wherein each of the pluralityof data structures contains information associated with local attributesof the associated decomposition component; reducing, by the informationprocessor, the size of the information contained in the plurality ofdata structures based on the information contained in the plurality ofdata structures; storing, by the information processor, the reduced-sizeinformation in a storage medium accessible by the information processor;and automatically diagnosing, using the information processor and basedon the plurality of associated data structures, a physiologicalcondition associated with the quasi-periodic waveform, and generatingrelevant information from the diagnosis.
 4. The computer-implementedmethod of claim 3, wherein the size of the information is reduced byselecting a subset of the information in each of a plurality of theplurality of data structures based on at least one attribute, andwherein the at least one attribute is selected from a group consistingof: a phase-label attribute of a fractional-phase representation; anabscissa attribute of the fractional-phase representation; an ordinateattribute of the fractional-phase representation; an abscissa-intervalattribute of an interval over which a fractional phase is active; adata-structure-label attribute identifying a particular data structurefrom within a plurality of data structures; a component-label attributeidentifying a decomposition component from a plurality of decompositioncomponents; a waveform-label attribute identifying the quasi-periodicwaveform; a local-period attribute representing a local period of thedecomposition component; a relative-ordinate attribute representing arelative measure between a first ordinate value associated with theactive fractional phase, and a second ordinate value associated with atleast one of a decomposition component and the quasi-periodic waveform;an active-component-label attribute descriptor that indicates whetherthe decomposition component is active for an active fractional phase,based upon comparing the ordinate value to a predetermined threshold; aphase-label deviation attribute that includes an indication of deviationfrom an expected sequence of phase labels; and a moving-statisticattribute that includes a moving statistic over a group of ordinatevalues for a group of data structures surrounding each data structure.5. The computer-implemented method of claim 4, wherein the plurality ofdata structures each has an associated decomposition component basedupon the quasi-periodic waveform and an associated set offractional-phase attributes active over a range of abscissa valuesassociated with the quasi-periodic waveform; assigning the datastructure attributes to a reconstruction formula; and applying thereconstruction formula over the associated range of abscissa valueswhere the associated fractional phase is active to substantiallyreconstruct the associated component signal at selected sample pointsalong the associated abscissa interval.
 6. The computer-implementedmethod of claim 3, wherein the size of the information is reduced byselecting a subset of the information in each of a plurality of theplurality of data structures based on an ordinate attribute of thefractional-phase representation of the decomposition component.
 7. Thecomputer-implemented method of claim 3, wherein the size of theinformation is reduced by selecting a subset of the information in eachof a plurality of the plurality of data structures based on upon anactive-component-label attribute descriptor for the correspondingdecomposition component.
 8. The computer-implemented method of claim 3,wherein the size of the information is reduced by performing datacompression on the information contained in fields comprising theplurality of data structures.
 9. The computer-implemented method ofclaim 3, wherein the reduced-size information is wirelessly transmittedto a receiver.
 10. The computer-implemented method of claim 3, whereinthe quasi-periodic waveform includes a cardiac waveform.
 11. Thecomputer-implemented method of claim 3, wherein the quasi-periodicwaveform signal includes an EEG signal.
 12. The computer-implementedmethod of claim 3, wherein the quasi-periodic waveform signal includesan EMG signal.
 13. The computer-implemented method of claim 3, whereinthe quasi-periodic waveform signal includes a respiratory signal. 14.The computer-implemented method of claim 3, further comprising: usingthe computer, arranging the plurality of associated data structures intoa plurality of arranged data structures by: arranging at least some ofthe plurality of data structures into a first arranged data structure,wherein the plurality of data structures are arranged in the firstarranged data structure according to their associated component, and theassociated fractional-phase values associated with the at least some ofthe plurality of data structures within the first arranged datastructure are coincidently active over at least one abscissa value ofthe corresponding range of abscissa values; and arranging at least someof the plurality of data structures into a second arranged datastructure, wherein the plurality of data structures are arranged in thesecond arranged data structure according to their associated component,and the associated fractional-phase values associated with the at leastsome of the plurality of data structures within the second arranged datastructure are coincidently active over a corresponding range of abscissavalues for a second associated quasi-periodic waveform; using thecomputer, generating a plurality of states, each state including one ofthe plurality of arranged data structures, the plurality of stateshaving a sequence; selecting a subset of states from the plurality ofstates; and storing the selected states and the sequence in a storagedevice accessible by the computer.
 15. The computer-implemented methodof claim 14, further comprising: assigning to the states a referenceabscissa value based upon the values of the abscissa attributes of theirassociated data structures; and arranging the plurality of states into astate sequence according to their respective reference abscissa values.16. The computer-implemented method of claim 3, wherein the size of theinformation is reduced by selecting a subset of the information in eachof a plurality of the plurality of data structures based on at least oneattribute.
 17. The computer-implemented method of claim 3, wherein thesize of the information is reduced by selecting a subset of the datastructures selected from the plurality of data structures.
 18. Thecomputer-implemented method of claim 3, wherein the size of theinformation is reduced by selecting a subset of the information in eachof a plurality of the plurality of data structures based on an abscissaattribute of the fractional-phase representation of the decompositioncomponent.
 19. The computer-implemented method of claim 3, furthercomprising: displaying a visualization of the relevant information. 20.A computer-implemented method comprising: obtaining a plurality of datastructures each having an associated decomposition component based upona quasi-periodic waveform and an associated set of fractional-phaseattributes active over a range of abscissa values associated with thequasi-periodic waveform, wherein the quasi-periodic waveform includes aphysiological signal; assigning the data structure attributes to areconstruction formula; applying, using an information processor, thereconstruction formula over the associated range of abscissa valueswhere the associated fractional-phase attributes are active tosubstantially reconstruct an associated component signal of thequasi-periodic waveform at selected sample points along the associatedabscissa interval; and automatically diagnosing, using the informationprocessor and based on the plurality of associated data structures, aphysiological condition associated with the quasi-periodic waveform, andgenerating relevant information from the diagnosis.
 21. Thecomputer-implemented method of claim 20, wherein a plurality of datastructures associated with a plurality of two or more decompositioncomponents are obtained and employed to substantially reconstruct theplurality of two or more associated component signals based upon the setof associated data structures.
 22. The computer-implemented method ofclaim 21, wherein the plurality of two or more reconstructed componentsignals are linearly combined to substantially reconstruct thequasi-periodic waveform.
 23. The computer-implemented method of claim20, further comprising: displaying a visualization of the relevantinformation.
 24. A non-transitory computer-readable medium havinginstructions stored thereon, wherein the instructions when executed on asuitable information processor, perform a method comprising: obtaining adigitized signal having a series of digital values of a quasi-periodicwaveform, wherein the quasi-periodic waveform includes a physiologicalsignal; performing a decomposition on the digitized signal to obtaindecomposition components of the digitized signal; analyzing, using aninformation processor, the decomposition components through afractional-phase transformation and forming a plurality of associateddata structures, wherein each of the plurality of data structurescontains information associated with local attributes of the associateddecomposition component; reducing, by the information processor, thesize of the information contained in the plurality of data structuresbased on the information contained in the plurality of data structures;storing, by the information processor, the reduced-size information in astorage medium accessible by the information processor; andautomatically diagnosing, using the information processor and based onthe plurality of associated data structures, a physiological conditionassociated with the quasi-periodic waveform, and generating relevantinformation from the diagnosis.
 25. A non-transitory computer-readablemedium having instructions stored thereon, wherein the instructions whenexecuted on a suitable information processor, perform a methodcomprising: obtaining a plurality of data structures each having anassociated decomposition component based upon a quasi-periodic waveformand an associated set of fractional-phase attributes active over a rangeof abscissa values associated with the quasi-periodic waveform, whereinthe quasi-periodic waveform includes a physiological signal; assigningthe data structure attributes to a reconstruction formula; applying,using an information processor, the reconstruction formula over theassociated range of abscissa values where the associatedfractional-phase attributes are active to substantially reconstruct anassociated component signal of the quasi-periodic waveform at selectedsample points along the associated abscissa interval; and automaticallydiagnosing, using the information processor and based on the pluralityof associated data structures, a physiological condition associated withthe quasi-periodic waveform, and generating relevant information fromthe diagnosis.
 26. An apparatus comprising: a computer that includes astorage device; a receiver unit in the computer configured to obtain adigitized signal that includes a series of digital values of aquasi-periodic waveform, wherein the quasi-periodic waveform includes aphysiological signal; a decomposition unit in the computer configured toperform a decomposition on the digitized signal to obtain decompositioncomponents of the digitized signal; a fractional-phase transformationunit in the computer, configured to process the decomposition componentsthrough a fractional-phase transformation, and to form a plurality ofassociated data structures, wherein each of the plurality of datastructures contains information associated with local attributes of theassociated decomposition component; a reduction unit in the computerconfigured to reduce the size of the information contained in theplurality of data structures based on the information contained in theplurality of data structures; a storage unit in the computer configuredto store the reduced-size information in the storage device; and adiagnostic unit in the computer configured to automatically diagnose,based on the plurality of associated data structures, a physiologicalcondition associated with the quasi-periodic waveform, and to generaterelevant information from the diagnosis.